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6d ago

Machine Learning Engineer

UCSF Campus BU San Francisco, CA

San Francisco, CA

6d ago

$101k - $216k/yr Full time Ai Ml Artificial Intelligence Cloud Operations

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Job Description

UCSF Campus BU is hiring a grant-funded Machine Learning Engineer for an 18-month Limited Appointment to support the APeX Enabled Research (AER) team. This hybrid role in San Francisco focuses on building and maintaining data pipelines and infrastructure that enable the deployment and continuous monitoring of ML and generative AI tools, including work connected to HIPAC and productionization in the EHR environment.

Role Summary

This position will lead the development, implementation, and upkeep of data pipelines and supporting infrastructure for deploying and continuously monitoring Machine Learning (ML) and generative Artificial Intelligence (AI) tools within the UCSF APeX Enabled Research (AER) team. The role emphasizes managing and optimizing HIPAC data and monitoring pipelines, as well as productionizing AI/ML tools for use in UCSF Health’s EHR.

Key Responsibilities

Lead the development, implementation, and maintenance of data pipelines and infrastructure supporting the deployment and continuous monitoring of ML and generative AI tools within the UCSF APeX Enabled Research (AER) team.
Manage and optimize the data and monitoring pipelines of the Health IT Platform for Advanced Computing (HIPAC).
Implement new data integrations.
Enhance HIPAC’s ETL functionalities.
Productionize AI/ML tools created by UCSF data scientists and researchers.
Design and implement metrics to continuously monitor AI/ML tools deployed at UCSF Health.
Contribute across workstreams including data integrations, HIPAC ETL enhancements, AI/ML productionization, and monitoring metrics for AI/ML tools at UCSF Health.

Required Qualifications

Bachelor’s degree in Computer Science, Computer Engineering, or related area, and/or equivalent experience or training.
6 years of experience in roles with increasing responsibility designing, implementing, and maintaining complex AI/ML applications.
Proficiency in MLOps, Python, SQL, and CI/CD is required.
Experience with data analysis and machine learning tools including Jupyter, Pandas, scikit-learn, NumPy/SciPy, and PyTorch.
Demonstrated advanced knowledge of the full software development lifecycle.
Advanced experience with Python, including clean, efficient, production-level code.
Advanced experience with SQL (e.g., SQLServer, PostgreSQL).
Demonstrated experience deploying, monitoring, and maintaining AI/ML models and pipelines.
Advanced experience in database systems, data warehousing solutions, and understanding of ETL pipelines.
Advanced experience designing, building, or maintaining data infrastructure for efficient ML training and inference.
Demonstrated experience working with MLOps, DevOps, and CI/CD toolsets.
Experience developing complex, automated testing.
Experience with cloud-based architecture in AWS, GCP, or Azure.
Demonstrated effective communication and interpersonal skills, including translating technical information for technical and non-technical audiences at multiple organizational levels.
Self-motivated approach with the ability to work independently and as part of a team, learn effectively, and meet deadlines.
Demonstrated broad problem-solving skills.
Demonstrated ability to interface with management on a regular basis.
Excellent project leadership and management skills.

Technologies

Python
SQL
CI/CD
Jupyter
Pandas
scikit-learn
NumPy
SciPy
PyTorch
MLOps
DevOps
SQLServer
PostgreSQL
AWS
GCP
Azure

Preferred Qualifications

Master’s degree or PhD in Computer Science, Computer Engineering, or related area, and/or equivalent experience or training.
Experience with large language models and other generative AI technologies, especially supporting the deployment of GenAI-based tools in a production environment.
Familiarity with data visualization tools (e.g., Tableau).
Experience with Epic data structures.
Epic Clarity Certification.
Cloud Development certifications.

Epic Data Models and Certification

This role requires a deep understanding of Epic data models (Clarity and Caboodle). Successful candidates either have Epic Clinical/Clarity data model certification or are able to obtain the certification shortly after onboarding.

Additional Job Information

Grant funded, 18-month Limited Appointment position.
Certain terms and conditions of employment, including rate of pay and benefits, are subject to negotiation with the appropriate union.
Flexible schedule with 2 days on-site.
Must be available for lab on-site meetings that may occur once per week or bi-weekly, based on departmental needs.

Location and Compensation

Location: San Francisco, CA (hybrid).
Salary: USD 101,000 - 216,000 per year.
Minimum Experience: 6 years.

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6d ago

Senior, Machine Learning Engineer

Walmart Sunnyvale, CA

Sunnyvale, CA

6d ago

$143k - $286k/yr Full time Senior Ai Ml Artificial Intelligence

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Job Description

Design and deliver scalable machine learning systems with a focus on automated model training optimization and experimentation for enterprise AI governance.

Responsibilities

Partner with engineers, data scientists, and product teams to deliver machine learning features and models.
Design and build auto-research capabilities that automate model training, optimization, experimentation, and evaluation.
Enable iterative hyperparameter tuning and experiment execution against defined objective metrics.
Identify and surface best-performing models with minimal manual intervention to speed up the machine learning development lifecycle.
Implement, test, and support machine learning services, APIs, and data pipelines to maintain quality and reliability.
Apply MLOps practices including CI/CD, model versioning, and automated retraining to sustain performance.
Perform rigorous evaluation, monitoring, and maintenance to address model drift and performance issues.
Ensure secure, compliant machine learning aligned with enterprise AI governance standards.

Requirements

Experience designing, developing, and deploying scalable machine learning systems using frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Strong programming skills in Python, SQL, Java, and C++ for production-ready machine learning applications.
Hands-on experience operating agentic or autonomous research systems.
Experience running automated experimentation against defined objective metrics through AutoResearch loops.
Knowledge of the end-to-end machine learning lifecycle: data sourcing, feature engineering, model training, evaluation, deployment, and monitoring.
MLOps knowledge including CI/CD, model versioning, automated retraining, and telemetry for reliability.
Ability to implement secure, compliant machine learning pipelines that align with enterprise AI governance standards.
Demonstrated collaboration and mentorship within cross-functional teams to drive technical excellence.

Technologies

TensorFlow, PyTorch, Scikit-learn
Python, SQL, Java, C++
CI/CD, AutoResearch, telemetry

Education & Experience

Minimum experience: 3 years.
Minimum qualifications (Option 1): Bachelor’s degree in computer science, computer engineering, computer information systems, software engineering, or related area, plus 3 years’ experience in software engineering, machine learning engineering, AI systems, or related area.
Minimum qualifications (Option 2): 5 years’ experience in software engineering, machine learning engineering, AI systems, or related area.

Preferred Qualifications

Master’s degree in a related field and 1 year’s experience in software engineering, machine learning engineering, AI systems, or related area.
Prior experience with accessibility best practices.
Prior experience creating inclusive digital experiences, including knowledge of implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA, assistive technologies, and integrating accessibility into digital experiences.

Benefits

Health benefits: medical, vision, and dental coverage.
Financial benefits: 401(k), stock purchase, and company-paid life insurance.
Paid time off: PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting.
Other benefits: short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more.
Live Better U: Walmart-paid education benefit program for full-time and part-time associates in Walmart and Sam’s Club facilities.
Tuition, books, and fees are paid by Walmart.

Work Location

Sunnyvale, CA, United States (onsite)
Primary location: 815 Eleventh Ave, Sunnyvale, CA 94089-4731, United States of America

Salary: USD 143,000 - 286,000 per year.

About the Team

Develops foundational models for Walmart’s e-commerce platform using large-scale user interactions to create representations of users, products, and search queries.
Supports scalable search, advertising, recommendation, and personalization solutions.
Applies data science, machine learning, and software engineering to optimize model performance, training, and deployment.
Focuses on data quality, advanced analytics, and impactful visualization to deliver actionable insights that influence key business outcomes.
Leverages cross-functional collaboration for innovation and continuous improvement in a dynamic technology environment.

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6d ago

Staff Machine Learning Engineer

Pinterest San Francisco, CA

San Francisco, CA

6d ago

$189k - $390k/yr Full time Manager Ai Ml Artificial Intelligence

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Job Description

Pinterest is seeking a Staff Machine Learning Engineer for Growth Content Science in San Francisco, working on complex models and production systems that support notification content ranking in a real-time environment.

Responsibilities

Lead multiple technical sub-areas within Growth content science effectively.
Drive Growth ML model improvements through an A/B test driven innovation loop, from ideation through production launches.
Own the health of the ML ecosystem, including training pipelines, feature and serving infrastructure, evaluation frameworks, monitoring, and A/B test setup.
Elevate technical excellence through mentorship, reviews, evaluations, documentation, and standard setting.
Collaborate with colleagues and stakeholders to address large, cross-functional initiatives.
Work closely with other engineering leaders to establish technical directions and roadmaps.
Operate autonomously, take ownership in complex and evolving environments, and drive work effectively.
Identify and apply relevant state-of-the-art techniques from recent research, or collaborate on novel research directions.
Continuously optimize work by building and using cutting-edge AI tools.

Requirements

Graduate degree in Computer Science or in a related field.
Minimum of 8 years of experience in machine learning engineering, including hands-on experience building and shipping models for a large-scale, real-time, end-user production recommender system.
Expertise in recommender modeling, including sequence modeling of user behavior with transformer architectures, DNN rankers, and generative and LLM-native recommenders.
Experience building and operating high-QPS, latency-constrained ML systems, including training and serving infrastructure, with ownership of cost, latency, and reliability tradeoffs.
Industry-grade software engineering experience that others use as an example, including conducting thorough code and design reviews.
Understanding of content personalization and content quality as one problem, including why cold-start and low-signal users break standard rankers.
Experience leading technical work across teams, making timely decisions on hard tradeoffs, and addressing situations where user experience or system health is at stake.
Experience mentoring junior engineers.
Outstanding written and verbal communication skills, particularly in cross-functional settings.
Demonstrated experience using AI to improve speed and quality in applied research.
Strong track record of publications and patents.

Work Location and Office Requirements

This role is based in San Francisco, CA and is onsite.
Pinterest notes that the ideal work environment can vary by department, and day-to-day expectations differ by organization or role.
For this position, you will need to be in the office for in-person collaboration 1-2 times per quarter and the role can be situated anywhere in the country.

Relocation

This position is not eligible for relocation assistance. Visit the PinFlex page to learn more about the working model.

Inclusion Commitment

Pinterest is an equal opportunity employer and makes employment decisions based on merit.
All qualified applicants receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, protected veteran status, physical or mental disability, medical condition, genetic information, or other unlawful considerations.
Qualified applicants are considered regardless of criminal histories, consistent with legal requirements.
If you require a medical or religious accommodation during the job application process, complete the provided form for support.

Additional Role Details

Experience level: 8+ years
Salary range: USD 189,308 - 389,753 per year

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6d ago

Machine Learning Engineer

Optimal Inc. Warren, MI

Warren, MI

6d ago

Full time Ai Ml Artificial Intelligence Data Science

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Job Description

Optimal Inc. is hiring a Machine Learning Engineer to build AI models that blend physical principles with multimodal data to simulate complex systems and forecast real-world behavior.

Responsibilities

Develop and implement Physics-Informed Neural Networks (PINNs) and physics-informed machine learning models.
Build multimodal models that integrate data types including images, sensor measurements, time-series inputs, and simulation outputs.
Develop predictive world models to learn system dynamics, simulate environments, and forecast future states.
Combine physics-based simulations with data-driven machine learning techniques.
Validate model accuracy, physical consistency, and performance using real-world or simulated datasets.

Requirements

Master’s or PhD in Computer Science, Machine Learning, Applied Mathematics, Physics, Engineering, or a related field.
Hands-on experience with PINNs or scientific machine learning.
Experience in multimodal modeling and/or world model development.
Strong Python programming skills, with experience using PyTorch or TensorFlow.
Knowledge of neural networks, physical systems, numerical methods, or simulation.

Technologies

Python
PyTorch
TensorFlow

Preferred Qualifications

Research or industry experience in automotive, robotics, autonomous systems, or physical AI.
Experience with sensor fusion, differential equations, simulation, or predictive dynamics models.
Relevant research publications or project experience.

Location & Work Mode

Warren, MI (onsite)

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6d ago

Staff Machine Learning Engineer

BD Laguna Hills, CA

Laguna Hills, CA

6d ago

$130k - $207k/yr Full time Ai Foundation Models Ai Ml Artificial Intelligence

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Job Description

BD is hiring a Staff Machine Learning Engineer to help develop clinically meaningful AI/ML solutions for continuous physiological monitoring, disease detection, and predictive clinical decision support. The role focuses on building end-to-end algorithms and deploying them across cloud systems and edge medical devices, working with multi-modal healthcare data to support monitoring and risk workflows.

What you’ll do

Lead the design, development, validation, and deployment of AI/ML algorithms for continuous physiological monitoring, novel vital sign and physiological parameter development, disease detection, risk stratification, and early warning systems.
Develop models using multi-modal sources, including physiological waveforms and time-series signals such as PPG, blood pressure, capnography, and other monitoring signals, along with bedside and wearable sensor data, electronic health records, and structured clinical data.
Translate clinical and physiological understanding into robust algorithm design, including feature selection, model architecture, performance targets, and clinically interpretable outputs.
Build end-to-end development pipelines for data ingestion, signal quality assessment, annotation, feature extraction, model training, validation, robustness testing, and post-market performance monitoring where applicable.
Partner with clinical, medical affairs, regulatory, quality, and product teams to align development with clinical context, usability, safety, and regulatory considerations.
Develop and optimize algorithms for cloud environments and for edge or embedded medical devices, accounting for latency, memory, compute, power, cybersecurity, and reliability constraints.
Support verification, validation, documentation, risk analysis, and design controls for regulated medical technology products.
Contribute to intellectual property generation, technical strategy, research roadmaps, and external scientific engagement.

What you bring

7+ years of industry experience in AI/ML, algorithm development, biomedical signal processing, healthcare analytics, medical devices, or related technical areas.
Master’s or PhD degree preferred in Electrical Engineering, Biomedical Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, Computational Biology, or a related engineering or quantitative field.
Deep expertise in machine learning and AI model development, including classical and modern approaches.
Hands-on experience with physiological time-series data, biomedical signals, medical device data, or clinical datasets.
Understanding of human physiology as it relates to patient monitoring.
Demonstrated experience developing algorithms from early feasibility through productization or deployment.
Experience with multi-modal healthcare data, including sensor signals, EHR data, imaging data, lab data, and clinical outcomes.
Strong programming skills in Python and experience with ML/AI frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent tools.
Experience building and deploying algorithms in cloud environments using Azure, AWS, or GCP.
Experience optimizing models for constrained environments, including embedded systems, edge devices, or near-real-time medical technology platforms.
Strong understanding of model evaluation, including sensitivity, specificity, AUROC, calibration, false alarm burden, clinical utility, robustness, and generalizability.
Excellent communication skills, with the ability to explain complex technical concepts to engineering, clinical, regulatory, and business stakeholders.

Technologies

Python; PyTorch; TensorFlow; scikit-learn
Azure; AWS; GCP
Transformer-based models and foundation-model approaches

Preferred qualifications

Experience developing AI/ML algorithms for regulated medical devices, digital health products, clinical decision support tools, or Software as a Medical Device (SaMD).
Familiarity with medical device development practices, including design controls, risk management, verification and validation, human factors, clinical performance evaluation, and regulatory documentation.
Experience with signal quality assessment, artifact detection, missing-data handling, sensor fusion, uncertainty quantification, and explainable AI.
Familiarity with MLOps and model lifecycle management, including version control, data governance, automated testing, and post-deployment monitoring.

Compensation and location

Location: USA CA - Irvine Laguna Canyon (onsite)
Salary range: USD 129,600 - 207,400 per yearly

Benefits

Medical, dental, vision, health savings and flexible spending accounts
Life and disability insurance
Wellness resources
Mental health support
Disease management programs
401(k) and deferred compensation programs
Financial counseling and DailyPay
Educational savings resources, educational assistance
Paid time off and company holidays
Parental leave, adoption assistance, backup care, caregiving support
Employee assistance programs
Commuter benefits
Pet insurance
Legal services and associate discount programs
Bereavement leave, military leave, personal leave, family and medical leave, jury duty leave

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6d ago

Senior Machine Learning Engineer–Field Engagement

Striveworks Washington, DC

Washington, DC

6d ago

$160k - $200k/yr Full time Artificial Intelligence Data Integration Data Pipeline

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Job Description

Lead customer-facing machine learning engineering for Striveworks’ Chariot AI product suite, spanning pilots to durable adoption.

Responsibilities

Develop deep, hands-on fluency across Chariot Core, Forge, and vertical solutions to run trials end to end and support credible product discussions
Drive fast time-to-value through hands-on onboarding, demos, and workshops tailored to each customer’s use case
Identify the highest-value customer problem and proactively bring it to working status by configuring relevant models, datasets, agents, and workflows
Co-build production AI systems and agents with customer engineering and ML teams, from initial prototype through rollout
Build ETL pipelines, scripts, and custom integrations to ingest customer data and connect their processes to Striveworks inference endpoints
Own the technical relationship during evaluation and early deployment by troubleshooting issues, removing technical blockers, and driving problems through resolution
Advise customers beyond initial deployment on architecture and implementation decisions, best practices, and relevant product capabilities and roadmap directions
Surface opportunities for deeper adoption and expansion for Sales and BD teams
Partner with Product and Engineering on high-value platform improvements needed for specific trials, and support launch activities for new features using field perspective
Feed field learnings back to Product, and build reusable assets such as example code, guides, documentation, and proof-of-concept templates

Requirements

Degree in a technical field (computer science, engineering, or similar)
6+ years of experience as an ML engineer, data scientist, forward-deployed engineer, or similar hands-on technical role, ideally with direct customer exposure
Strong fluency in AI and ML systems and the ability to collaborate across software engineering, data science, ML engineering, and infrastructure teams
Strong software engineering skills, including writing, reading, and debugging production-quality Python, plus building pragmatic integrations, prototypes, and tooling
Comfort with ambiguity: independently understand problems, form a plan, and execute without step-by-step direction
Experience deploying AI models and agents at scale
Experience with AI model and agent evaluation, observability, or guardrails
Strong customer communication skills and ability to build trust with technical stakeholders
US citizenship and eligibility/willingness to obtain and maintain a Secret (or above) US security clearance

Technologies

Python
ETL pipelines
vLLM
Kubernetes
APIs

What You’ll Build

Work directly with customers during trials and early deployments of Chariot, spanning Chariot Core (AIOps platform), Forge (agentic harness), and vertical solutions
Own the technical journey from first log in to real value by building integrations, pipelines, and configurations that help customers solve meaningful problems
Carry technical context from pilot through post-sale support to help evaluations become lasting adoption
Use field learnings to shape high-value work between engagements, including platform improvements, better onboarding and technical resources, and technical blogs/papers focused on applications or research

Benefits

Medical/dental/vision insurance
Voluntary life, long-term disability, accident, and hospital indemnity insurance
HSA and FSA (including dependent care FSA)
401(k) plan
Unlimited PTO
Paid parental leave

Location and Travel

Washington, DC (hybrid)
Hybrid/on-site work near customer sites in Washington, DC, Seattle, WA, or Raleigh-Durham, NC areas
Travel up to 25% of the time

Compensation

Anticipated base pay range: $160,000–$200,000/year
Total compensation includes competitive base salary, equity grants, and cash bonuses

Additional (Not Required, Preferred)

Experience deploying large language models using vLLM
Familiarity with cloud infrastructure, Kubernetes, APIs, distributed systems, or modern data and ML platforms
Experience leading, managing, or mentoring small, cross-functional engineering teams
Active Secret (or above) US security clearance

Equal Opportunity, E-Verify, and Accommodations

Equal Opportunity Employer; does not discriminate based on protected characteristics or other non-merit factors
No tolerance for discrimination or harassment
Application accommodation support via hr@striveworks.us
Participating employer in the E-Verify program
Employment eligibility verification required upon hire

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6d ago

Machine Learning Engineer

Unissant Ashburn, VA

Ashburn, VA

6d ago

$120k - $175k/yr Full time Ai Ml Analytics Artificial Intelligence

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Job Description

Unissant is seeking a Machine Learning Engineer to design, build, and maintain machine-learning models while supporting AI product development for a federal customer. This hybrid role is based in Ashburn, Virginia, with onsite attendance required four days per week during the first year and as mission needs require.

In this position, you will work within a large Agile development team to turn data into reliable AI capabilities, from prototyping through deployment and ongoing monitoring. The work emphasizes model performance, data quality, and operational reliability for real-world use cases.

Responsibilities

Design, build, and maintain machine-learning models for AI applications.
Continuously improve model accuracy, efficiency, scalability, and reliability.
Analyze large datasets to identify patterns, trends, and actionable insights.
Develop hypotheses, design experiments, and validate findings using statistical methods.
Develop and maintain reliable data pipelines that support data quality and usability.
Collaborate with data-engineering teams to optimize data infrastructure.
Support AI product development from prototyping through deployment and monitoring.
Work with product managers, designers, and quality-assurance teams to integrate AI capabilities.
Implement monitoring and alerting for model performance and product KPIs.
Conduct ongoing model maintenance to ensure accuracy and operational reliability.
Collaborate within a large Agile development team.

Requirements

Experience supporting artificial-intelligence, machine-learning, or data-analytics projects.
Demonstrated experience developing and deploying AI/ML models in production environments.
Experience with product-development lifecycles and Agile methodologies is preferred.
Proficiency in Python, R, or Java.
Experience with machine-learning frameworks and libraries such as TensorFlow, PyTorch, or Scikit-learn.
Experience with MLOps pipelines and CI/CD.
Familiarity with SQL and NoSQL databases.
Familiarity with data-processing frameworks such as Apache Spark or Hadoop.
Experience with analytics and visualization tools such as Tableau, Power BI, Matplotlib, or Plotly.
Knowledge of AWS, Microsoft Azure, or Google Cloud for model deployment and data storage is preferred.
Strong analytical and problem-solving abilities with exceptional attention to detail.
Excellent interpersonal and cross-functional communication skills.
Ability to work independently and manage multiple projects simultaneously.
A bachelor’s degree in Computer Science, Data Science, Electrical Engineering, Physics, or a related technical field is required.
United States citizenship is required for this federal program.
Ability to obtain and maintain a Public Trust position and favorable suitability based on a U.S. Customs and Border Protection Background Investigation.
Candidates may begin with a limited CBP Background Investigation.

Technologies

Python, R, Java
TensorFlow, PyTorch, Scikit-learn
MLOps, CI/CD
SQL, NoSQL
Apache Spark, Hadoop
Tableau, Power BI, Matplotlib, Plotly
AWS, Microsoft Azure, Google Cloud

Work Location and Travel

Hybrid position based in Ashburn, Virginia.
Onsite attendance required four days per week during the first year and as mission needs require.

Work Environment

Primarily sedentary work performed in an office environment.
Flexibility may be required to support changing projects and mission priorities.

Communication

Excellent written and verbal communication skills.
Ability to communicate technical information to both technical and nontechnical audiences.
Ability to collaborate effectively with product, engineering, design, and quality-assurance teams.
Ability to work successfully in a large Agile team environment.