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Today

Staff Machine Learning Engineer

BD Laguna Hills, CA

Laguna Hills, CA

Today

$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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3d ago

Sr Machine Learning Engineer- Delivery Marketplace

Uber Sunnyvale, CA

Sunnyvale, CA

3d ago

$202k - $224k/yr Full time Backend Developer Ai Ml Artificial Intelligence

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Job Description

Uber’s Delivery Marketplace team is seeking a Senior Machine Learning Engineer to lead the design, development, and productionization of advanced machine learning models and matching algorithms. The role focuses on improving dispatch, ETA, pickup optimization, and trip reliability through large-scale, real-time multi-objective optimization systems.

Key Responsibilities

Drive technical strategy and roadmap ownership across multiple quarter horizons, including platform-level architecture decisions, executive communication and alignment, technical mentorship, and cross-team technical influence.
Develop novel online marketplace products that combine machine learning for prediction and forecasting with optimization to improve business efficiency and user experience.
Lead end-to-end design, development, and productionization of machine learning solutions for large-scale distributed systems serving billions of trips.
Develop transformer-based models, including pre-training and fine-tuning, to generate embeddings and natural language profiles that support Marketplace use cases.

Required Qualifications

Ph.D., M.S., or Bachelor in Computer Science, Mathematics with a focus on Machine Learning, or equivalent technical background with exceptional demonstrated impact.
3+ years of experience leading development and deployment of ML models in large-scale production environments at top-tier ML companies (for example, 1M+ predictions/sec or 100M+ users), with a track record of delivering outstanding business impact over multiple quarters.
Proficiency in programming languages such as Python, Scala, Java, or Go.
Proficiency with large-scale data systems (for example, Spark, Ray), real-time processing (for example, Flink), and microservices architectures.
Proficiency in building, training, productionizing, and monitoring ML solutions at scale, from offline pipelines to online serving and MLOps.
Deep understanding of modern machine learning algorithms.

Technologies

Python, Scala, Java, Go
Spark, Ray, Flink
Microservices architectures
Machine learning, deep learning
Causal modeling, reinforcement learning
Transformer-based models, embeddings, natural language profiles
MLOps

Compensation and Location

Location: Sunnyvale, CA (onsite)

Salary: USD 202,000 - 224,000 per year

Benefits

Eligible to participate in Uber’s bonus program.
May be offered an equity award and other types of compensation.
All full-time employees are eligible to participate in a 401(k) plan.
Eligible for various benefits.

Preferred Qualifications

Experience developing and deploying ML models for large-scale business problems.
Track record of translating complex business problems into technical solutions and driving cross-functional projects.
Excellent communication skills to lead initiatives across multiple product areas.
Proficiency in deep learning, causal machine learning, or reinforcement learning.
Proficiency with large embedding and language models.

Onsite Requirements

Offices remain key to collaboration and Uber’s culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, such as those at greenlight hubs, require full-time in-office presence.

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1mo ago

Data Scientist

FIS Global Atlanta, GA

Atlanta, GA

1mo ago Closed

Full time Artificial Intelligence Big Data Bigdata

This position is no longer accepting applications (closed Sep 28, 2026).

Job Description

FIS Global is hiring a Data Scientist to help build and scale data-driven exploratory analysis, predictive models, and AI solutions across financial services business areas. This onsite role in Atlanta, GA focuses on supporting the full machine learning lifecycle and turning analytical results into practical, product-ready recommendations.

You will work on advanced analytics spanning model development through monitoring, with an emphasis on measurable business impact in payments and broader financial services.

What you’ll do

Participate in the end-to-end design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions.
Use data structures and algorithms to analyze, prepare, and assemble datasets from standard and novel data sources for analytical solutions.
Apply machine learning, predictive analytics, NLP, and emerging AI approaches such as GenAI and agentic techniques to address complex problems in the payments and financial services ecosystem.
Design and run experiments, hypothesis testing frameworks, and statistical analyses to evaluate business strategies, product enhancements, and operational improvements.
Establish and promote best practices across feature engineering, experimentation, model governance, and MLOps within the organization.
Stay current with industry trends in machine learning, AI, generative AI, and financial services analytics, and share relevant innovations with the team.

What you’ll bring

Master’s degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative field.
1-3 years of experience developing and deploying end-to-end machine learning, predictive analytics, and data science solutions in Payments, Banking, or Financial Services.
Strong proficiency in Python and SQL, with experience using big data tools such as Spark and PySpark as a plus.
Hands-on experience with data wrangling, feature engineering, and model development using Pandas, NumPy, scikit-learn, Matplotlib, Seaborn, or Plotly.
Proven experience building and deploying machine learning models in production or near-production settings.
Proficiency with data visualization and business intelligence tools, such as Tableau or equivalent.
Strong analytical thinking and problem-solving skills, including the ability to translate ambiguous business questions into rigorous analytical frameworks.
Ability to collaborate with product, engineering, and business stakeholders.

Technologies you may work with

Python, SQL, Spark, PySpark
Pandas, NumPy, scikit-learn
Matplotlib, Seaborn, Plotly
Tableau
NLP, GenAI, Agentic
MLflow, Model Registry, MLOps
Databricks (including Databricks platform), AWS
Transformer Models

Benefits

A voice in the future of fintech
Always-on learning and development
Collaborative work environment
Opportunities to give back
Competitive salary and benefits

Nice to have

Experience specifically within the Payments, Banking, or Financial Services industry.
Hands-on experience with the Databricks platform, including MLflow, Model Registry, collaborative notebooks, and MLOps workflows.
Experience deploying cloud-native machine learning solutions, particularly in AWS environments.
Familiarity with emerging advancements in Transformer Models and agentic AI.
Knowledge of model governance, regulatory compliance, and MLOps best practices in regulated financial services environments.

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Transformer Models Data Jobs

Staff Machine Learning Engineer

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