DataJobs.io - Jobs in data science, machine learning and analytics.

Latest Jobs

1mo ago

Machine Learning Engineer 5

Capital One McLean, VA

McLean, VA

1mo ago Closed

$230k - $262k/yr Full time Ai Ml Artificial Intelligence Big Data

This position is no longer accepting applications (closed Oct 23, 2026).

Job Description

Capital One is hiring a senior-level Machine Learning Engineer to build and deploy proprietary, AI-powered risk management solutions.

Responsibilities

Design, build, and/or deliver ML models and components to solve real business problems in collaboration with Product and Data Science teams
Build and scale large multi-tenant platforms for large-footprint ML training and/or serving
Guide ML infrastructure decisions using knowledge of ML modeling topics such as model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Work in a cross-functional Agile team to create and improve software enabling big data and ML applications
Retrain, maintain, and monitor ML models in production
Leverage and/or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
Construct optimized data pipelines to feed ML models
Apply CI/CD best practices, including test automation and monitoring, to support successful deployment of ML models and application code
Maintain well-managed code to reduce vulnerabilities, govern ML from a risk perspective, and apply Responsible and Explainable AI best practices
Use programming languages including Python, Scala, or Java

Requirements

Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 6 years of experience programming with Python, Java, Golang, or C++
At least 6 years of Machine Learning experience using industry-standard frameworks PyTorch or Tensorflow and libraries Pandas, NumPy, Scikit-learn
At least 6 years of experience using and operating large-scale distributed systems (e.g., Spark, Ray) to prepare AI/ML data
At least 4 years of experience deploying and operating ML solutions in production, including operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large-scale containerized ML systems

Technologies

Python, Scala, Java, Golang, C++
PyTorch, Tensorflow
Pandas, NumPy, Scikit-learn
Spark, Ray
AWS, GCP, Azure
Kubernetes
CI/CD, Agile

Benefits

Performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being

Preferred Qualifications

Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field
5+ years of experience optimizing ML algorithms, configurations, and infrastructure
5+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD
5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation
5+ years of experience with ML techniques (supervised, semi-supervised, unsupervised, reinforcement learning, etc.), model types (regression, classification, clustering, etc.), model architectures (RNNs, CNNs, LSTMs, Transformers), and training/evaluation concepts (loss function, hyperparameters, regularization, diagnosing underfitting and overfitting)
5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
Ability to communicate complex technical and ML concepts clearly to a variety of audiences

Additional Information

Location: McLean, VA (onsite)
Salary (USD): $229,900 - $262,400 per year
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position
Expected application review window: minimum of 5 business days
No agencies please
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination
Capital One promotes a drug-free workplace
Capital One will consider qualified applicants with a criminal history consistent with applicable law
Accommodation request: 1-800-304-9102 or RecruitingAccommodation@capitalone.com
Recruiting process technical questions: Careers@capitalone.com

View full job page →

1mo ago

Machine Learning Engineer 4

Capital One McLean, VA

McLean, VA

1mo ago Closed

$197k - $225k/yr Full time Artificial Intelligence Big Data Data Pipeline

This position is no longer accepting applications (closed Oct 23, 2026).

Job Description

Capital One is hiring a Machine Learning Engineer to design, build, deliver, and productionize machine learning models and supporting components. In this onsite role in McLean, VA, you will work closely with Product and Data Science teams to deploy ML solutions at scale while supporting responsible, explainable, and well-governed AI practices.

What you’ll do

Design, build, and/or deliver ML models and components that address real business problems in collaboration with Product and Data Science teams
Shape ML infrastructure decisions using knowledge of modeling techniques and common issues, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployments
Work within a cross-functional Agile team to create and improve software that supports advanced big data and ML applications
Retrain, maintain, and monitor models in production
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
Construct optimized data pipelines that feed ML models
Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful production releases
Manage code to reduce vulnerabilities, ensure models are governed from a risk perspective, and follow best practices in Responsible and Explainable AI
Use programming languages such as Python, Scala, or Java

Requirements

Bachelor’s degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 4 years of experience programming with Python, Java, Golang, or C++
At least 4 years of Machine Learning experience using industry frameworks PyTorch or TensorFlow and libraries such as Pandas, NumPy, Scikit-learn
At least 4 years of experience working with and operating large-scale distributed systems (such as Spark and Ray) to prepare AI/ML data
At least 2 years deploying and operating Machine Learning solutions in production, including production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large-scale containerized ML systems

Technologies

Python, Scala, Java, Golang, C++, PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn, Spark, Ray, AWS, GCP, Azure, Kubernetes

Compensation and location

McLean, VA (onsite): $197,300 - $225,100 per year for Machine Learning Engineer 4
Eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI)

Benefits

Comprehensive, competitive, and inclusive set of health, financial and other benefits

Preferred qualifications

Master’s or doctoral degree in computer science, electrical engineering, mathematics, or related field
3+ years of experience optimizing ML algorithms, configurations, and infrastructure
3+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD
3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans
3+ years of experience with ML techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning) across model types (Regression, Classification, Clustering), model architectures (RNNs, CNNs, LSTMs, Transformers), and training concepts (loss function, hyperparameters, regularization) including evaluating model accuracy and diagnosing issues such as underfitting and overfitting
3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation
Authored/co-authored a paper on an ML technique, model, or proof of concept

Additional information

Capital One will not sponsor a new applicant for employment authorization or provide immigration-related support for this position
This role is expected to accept applications for a minimum of 5 business days
No agencies please
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable laws
Capital One promotes a drug-free workplace
If you visited the website and need an accommodation, contact Capital One Recruiting at 1-800-304-9102 or RecruitingAccommodation@capitalone.com
For technical support or recruiting process questions, email Careers@capitalone.com
Capital One does not guarantee third-party products, services, educational tools, or other information available through the site
Capital One Financial is made up of several different entities (Canada/UK/Philippines posting entity notes)

View full job page →

1mo ago

Machine Learning Engineer 5

Capital One McLean, VA

McLean, VA

1mo ago Closed

$230k - $262k/yr Full time Ai Ml Artificial Intelligence Automation

This position is no longer accepting applications (closed Oct 23, 2026).

Job Description

Capital One is seeking Machine Learning Engineers to design, build, and deliver production ML capabilities across large-scale platforms.

Responsibilities

Design, build, and/or deliver ML models and components to address real-world business problems in partnership with Product and Data Science teams
Build and scale large multi-tenant ML platforms to support high-footprint model training and/or serving at scale
Apply ML modeling knowledge to guide ML infrastructure decisions, including model selection, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Collaborate in a cross-functional Agile team to create and enhance software for big data and ML applications
Retrain, maintain, and monitor models in production
Leverage or build cloud architectures, technologies, and/or platforms to deliver optimized ML models at scale
Construct optimized data pipelines that feed ML models
Use CI/CD best practices, including test automation and monitoring, to support successful deployment of ML models and application code
Manage code to reduce vulnerabilities, ensure risk-governed models, and follow best practices for Responsible and Explainable AI
Use programming languages such as Python, Scala, or Java

Requirements

Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 6 years of experience programming with Python, Java, Golang, or C++
At least 6 years of Machine Learning experience using industry-standard frameworks PyTorch or TensorFlow plus libraries (Pandas, NumPy, Scikit-learn)
At least 6 years of experience using and operating large-scale distributed systems (Spark, Ray) to prepare AI/ML data
At least 4 years of experience deploying and operating ML solutions in production and operating production services in the cloud (AWS, GCP, Azure), including Kubernetes to manage containerized ML systems at scale

Technologies

Python, Scala, Java, Golang, C++
PyTorch, Tensorflow
Pandas, NumPy, Scikit-learn
Spark, Ray
AWS, GCP, Azure
Kubernetes

Benefits

Comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being
Performance-based incentive compensation (may include cash bonus(es) and/or long term incentives (LTI))

Preferred Qualifications

Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field
5+ years of experience optimizing ML algorithms, configurations, and infrastructure
5+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD
5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation
5+ years of experience applying ML techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.), model types (Regression, Classification, Clustering, etc.), model architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating accuracy and diagnosing issues such as underfitting and overfitting
5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
Ability to communicate complex technical and machine learning concepts clearly to varied audiences

Location: McLean, VA (onsite)

Compensation: USD 229,900 - 262,400 per year (Machine Learning Engineer 5)

Expected to accept applications for a minimum of 5 business days
No agencies please
Equal Opportunity Employer (EOE, including disability/vet), committed to non-discrimination per applicable laws
Drug-free workplace
Capital One considers applicants with criminal history in a manner consistent with applicable laws
Accommodation request contact: 1-800-304-9102 or RecruitingAccommodation@capitalone.com
Technical support/questions about recruiting process: Careers@capitalone.com
Capital One does not endorse or guarantee third-party products or information available through this site
Posting location may correspond to Capital One Canada, Capital One Europe, or Capital One Philippines Service Corp. (COPSSC)

View full job page →

1mo ago

Machine Learning Engineer

Qualcomm San Diego, CA

San Diego, CA

1mo ago Closed

$123k - $184k/yr Full time Artificial Intelligence Data Science Engineer

This position is no longer accepting applications (closed Oct 23, 2026).

Job Description

Build and deploy AI and Generative AI capabilities that support Nimbus initiatives and help advance mobile, edge, auto, and IoT products.

Responsibilities

Collaborate on the design, development, and delivery of AI-powered capabilities aligned to Nimbus product objectives and customer needs.
Develop, evaluate, and deploy AI and Generative AI solutions for network optimization, Digital Twin technologies, and other strategic AI-driven products.
Partner with engineering, product, and domain experts to translate business and customer requirements into practical AI-enabled solutions.
Explain technical concepts, project status, and recommendations clearly to both technical and non-technical audiences.
Track emerging AI technologies, industry trends, and best practices to identify opportunities across Nimbus initiatives.
Use sound software engineering and AI development practices to deliver scalable, maintainable, high-quality solutions.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
Working knowledge of modern AI/ML concepts, including model development, evaluation, deployment, and operationalization.
Experience delivering AI-enabled software products or features from concept through production deployment.
Familiarity with AI concepts such as agentic AI frameworks, knowledge management systems, and other LLM-based applications.
Strong communication, collaboration, analytical, and problem-solving skills.
B.S. in Computer Science, Engineering, Data Science, or a related technical field.

Preferred Qualifications

1+ years of hands-on experience developing, fine-tuning, or deploying AI/ML models.
M.S. or Ph.D. in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related discipline.
Experience with cloud-based AI platforms, MLOps practices, data engineering, or large-scale data processing systems.
Familiarity with telecommunications, wireless networking, Digital Twin technologies, network automation, or intelligent network-management systems.
Experience with Generative AI, agentic systems, multi-agent frameworks, LLM applications, or AI-assisted software development.
Demonstrated interest in emerging AI technologies and applying them to real-world engineering and product challenges.

Benefits

Competitive annual discretionary bonus program.
Opportunity for annual RSU grants.
Highly competitive benefits package.

Compensation

Annual pay range: USD 122,800 - 184,200.
Opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for the annual bonus).

Location & Employment

San Diego, CA (onsite).
Minimum experience: 1 years.

View full job page →

1mo ago

Machine Learning Engineer 4

Capital One New York, NY

New York, NY

1mo ago Closed

$215k - $246k/yr Full time Ai Ml Artificial Intelligence Big Data

This position is no longer accepting applications (closed Oct 23, 2026).

Job Description

Capital One is hiring a Machine Learning Engineer 4 to support Enterprise Platforms Technology (EPTech) and the Marketing and Messaging team. The role focuses on designing, building, deploying, and monitoring machine learning models and pipelines at scale, with responsible AI practices and cloud-based architectures.

Role Focus

This position supports EPTech and the Marketing and Messaging organization. The Marketing and Messaging team delivers hyper-personalized messages and experiences that improve customer engagement, attract prospects, and drive increasing business value. The team also builds scalable platforms for omnichannel message delivery across owned and paid Adtech channels.

Key Responsibilities

Design, build, and/or deliver machine learning models and components that address real-world business problems in collaboration with Product and Data Science teams.
Apply knowledge of ML modeling techniques and challenges to inform infrastructure decisions, including model and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
Participate in a cross-functional Agile team to create and improve software supporting big data and ML applications.
Retrain, maintain, and monitor models in production.
Use or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines that feed ML models.
Apply CI/CD best practices, including test automation and monitoring, to support successful deployment of ML models and application code.
Help ensure code is well-managed to reduce vulnerabilities, models are governed from a risk perspective, and ML work follows Responsible and Explainable AI best practices.
Use programming languages such as Python, Scala, or Java.

Required Qualifications

Bachelor’s degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering).
At least 4 years of experience programming with Python, Java, Golang, or C++.
At least 4 years of machine learning experience with PyTorch or TensorFlow and libraries including Pandas, NumPy, and Scikit-learn.
At least 4 years of experience using and operating large-scale distributed systems such as Spark and Ray to prepare AI/ML data.
At least 2 years of experience deploying and operating machine learning solutions in production and operating production services in cloud environments (AWS, GCP, Azure), including using Kubernetes for large-scale containerized ML systems.
Master’s or Doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
3+ years of experience optimizing ML algorithms, configurations, and infrastructure.
3+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD.
3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation.
3+ years of experience with ML techniques including supervised, semi-supervised, unsupervised learning, and reinforcement learning, along with model types such as regression, classification, and clustering.
3+ years of experience with model architectures including RNNs, CNNs, LSTMs, and Transformers, plus training concepts like loss functions, hyperparameters, and regularization, and evaluating model accuracy while diagnosing underfitting and overfitting.
3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation.
Authored or co-authored a paper on a machine learning technique, model, or proof of concept.

Technologies

Programming: Python, Scala, Java, Golang, C++
Cloud and Infrastructure: AWS, GCP, Azure, Kubernetes
Machine Learning: PyTorch, Tensorflow
Data and Libraries: Pandas, NumPy, Scikit-learn
Distributed Systems: Spark, Ray
Delivery Practices: CI/CD
Model Architectures: RNNs, CNNs, LSTMs, Transformers

Compensation and Location

Location: New York, NY (onsite).

Salary range: USD 215,200 - 245,600 per year.

Note: Candidates hired to work in other locations will be subject to the pay range associated with that location.

Benefits

Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
Eligibility for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI).

Additional Information on Entities

Positions posted in Canada are for Capital One Canada.
Positions posted in the United Kingdom are for Capital One Europe.
Positions posted in the Philippines are for Capital One Philippines Service Corp. (COPSSC).

View full job page →

1mo ago

Principal Machine Learning Engineer, Matching and Recommendations

Bumble Inc. New York, NY

New York, NY

1mo ago Closed

$345k - $410k/yr Full time Ai Ml Artificial Intelligence Big Data

This position is no longer accepting applications (closed Oct 23, 2026).

Job Description

Bumble is seeking a Principal Machine Learning Engineer to define and lead ML strategy for next-generation matching and recommendation systems.

Responsibilities

Define and lead technical strategy for AI and ML systems that support recommendations, ranking, and personalization across Bumble products
Deliver measurable improvements in user engagement and safety
Design, develop, and deploy production-grade models using modern ML frameworks such as PyTorch, with focus on scalability and reliability in high-traffic environments
Build and deploy production AI Agents using raw and fine-tuned foundational LLMs, including sub-agents, tools, and MCP integrations
Architect end-to-end ML pipelines, integrating data processing (e.g., Spark, Airflow) with training, evaluation, and deployment workflows
Drive experimentation frameworks, including A/B testing and offline evaluation, to improve model performance and product outcomes
Partner cross-functionally with Product, Engineering, and Data leadership to convert business challenges into impactful ML solutions
Mentor and elevate senior individual contributors, supporting a culture of Excellence, Curiosity, and continuous learning across the ML community
Own complex and ambiguous problem spaces from insight to impact, adapting with an agile mindset
Champion responsible AI practices to embed fairness, transparency, and user safety into ML systems

Requirements

Typically requires 10–15 years of experience (alternative backgrounds considered when skills are equivalent)
Deep, hands-on expertise building and deploying large-scale ML systems in production
Python proficiency and experience with at least one major ML framework (e.g., PyTorch or TensorFlow)
Experience in recommendation systems, ranking models, and/or NLP
Expertise in prompting and fine-tuning LLMs, plus building production AI Agents
Proven ability to design scalable data and ML pipelines using tools such as Spark, Airflow, or similar distributed systems
Ability to operate as a senior individual contributor and influence technical strategy and decisions without direct authority
Demonstrated cross-functional collaboration and ownership of outcomes in complex organizational environments
Track record mentoring and uplifting others, role modeling Respect and Excellence while building inclusive, high-performing teams
Strong AI fluency to independently design, evaluate, and optimize ML systems and guide responsible, effective AI use by others

Technologies

Python
PyTorch
TensorFlow
Large Language Models (LLMs)
MCP
Spark
Airflow
A/B testing

Compensation

USD 345,000 - 410,000 per year
Final compensation determined based on qualifications, relevant experience, skill set, and other job-related factors

Inclusion at Bumble Inc.

Bumble Inc. is an equal opportunity employer and encourages candidates of all ages, colors, sexual orientations, gender identities, veterans, parents, people with disabilities, and neurodivergent people to apply
Reasonable adjustments are available to help candidates feel more confident during the process
Candidates may include pronouns in the application (e.g., she/her, he/him, they/them)

AI in Bumble Inc. Hiring

AI tools may support parts of recruitment, such as recording, transcribing, summarizing conversations, and highlighting resume-job alignment
Hiring decisions are made by people; AI supports efficiency and candidate experience, not evaluation or decision-making
AI-supported interviews and conversations are voluntary and do not affect candidacy
Opt out is available by informing the recruiter or interviewer at the start of a call or anytime during the conversation
Summaries and related data are retained only as needed per internal retention policies; transcription or summary deletion can be requested via the recruiter

Fraudulent Candidate Detection

The applicant tracking system analyzes signals tied to device, IP, email, and phone data to protect applicants and the hiring process
Signals are for internal review only and are not a definitive determination of identity or intent
No rejection, advancement, or impact occurs based solely on this analysis without human review

Location: New York, NY (onsite)

View full job page →

1mo ago

Senior Machine Learning Engineer

Zotec Partners Carmel, IN

Carmel, IN

1mo ago Closed

Full time Ai Ml Artificial Intelligence AWS

This position is no longer accepting applications (closed Oct 23, 2026).

Job Description

Zotec Partners is seeking a Senior Machine Learning Engineer to build and extend machine learning and AI systems that enable governed access to data and AI tooling. This onsite role in Carmel, IN focuses on shipping models, connecting large language models to Zotec data, and improving safe and accurate AI reasoning over that information.

Responsibilities

Design, develop, and maintain machine learning models and data integrations that support AI-driven products and internal tools
Connect large language models to real data by building and maintaining tools and integrations that enable accurate and safe querying and reasoning over Zotec data
Own the machine learning lifecycle, including problem framing, data preparation, training, evaluation, deployment, and monitoring
Architect scalable, cost-aware machine learning infrastructure on AWS using services such as SageMaker, Bedrock, and Lambda
Apply ontology, taxonomy, or knowledge-graph thinking to improve retrieval, reasoning, or data quality for AI systems

Requirements

Bachelor's degree required, preferably in a STEM field; Master's degree preferred
3 to 5 years delivering machine learning solutions at scale
Proficiency in Python
Proficiency in machine learning model development, training, and evaluation
Proficiency in working with structured and unstructured data at scale
Proficiency in system design for data and machine learning pipelines
Familiarity with AWS, including SageMaker, Bedrock, Lambda, and Redshift
Familiarity with Claude and other large language models
Familiarity with ontology, knowledge graphs, or structured data modeling
Familiarity with DevOps practices, including CI/CD pipelines and infrastructure as code (IaC)
Experience building and maintaining tools and integrations that connect large language models to real data sources
Experience deploying and monitoring machine learning models in production on AWS
Experience applying knowledge representation methods (ontologies, taxonomies, or graphs) to improve retrieval or reasoning
Experience instrumenting models and pipelines for observability and performance monitoring
Ability to communicate clearly with cross-functional teams, including Data Engineers, Data Scientists, Business Analysts, and other engineers
Comfort balancing technical depth with practical problem-solving across performance, scalability, and accuracy tradeoffs
Commitment to clean, maintainable code and mentoring through code reviews, documentation, and collaboration
Enjoyment of end-to-end collaboration with a supportive team on complex technical challenges
Understanding of quality standards, processes, compliance, and security as they relate to the healthcare industry, including general knowledge of HIPAA or PCI
Experience expanding LLM application patterns, including retrieval-augmented generation, prompt engineering, and evaluation frameworks
Experience using knowledge graphs and semantic data modeling to improve data retrieval and reasoning
Follows development methodologies including Scrum and Kanban