Lead Applied ML Engineer Job at Baptist Health (Remote)

Lead Applied ML Engineer

Role Summary: Lead AI Implementation: Drive the end-to-end development of produc...

Location

United States , Remote

Salary:

144000.00 - 186000.00 USD / Year

Baptist Health

Expiration Date

Until further notice

Requirements

Masters degree in Computer Science/Machine Learning or a minimum of 10 years equivalent professional experience
Must have experience in GCP
Proven team leadership background in machine learning and artificial intelligence with expertise in one or more of: computer vision, NLP, speech, optimization, deep learning, reinforcement learning, time series, generative models, signals, and distributed systems
Strong proficiency in ML modeling frameworks
Strong expertise in overall software development approach
Significant leadership experience in building end to end data systems
Advanced software engineering skills with proven experience crafting, prototyping, and delivering advanced algorithmic solutions
Proficiency in one or multiple machine learning languages (ex: Python) & development environments such as AWS Sagemaker
Minimum Required Experience: 10 years

Job Responsibility

Lead AI Implementation: Drive the end-to-end development of production-grade AI solutions, from LLM orchestration and backend APIs to interactive UI prototypes and automated deployment pipelines
Full-Stack Ownership: Take accountability for the technical lifecycle of AI products, ensuring they are scalable, secure, and seamlessly integrated into healthcare workflows
GenAI & Advanced Modeling: Develop and deploy advanced Generative AI applications using RAG patterns and model fine-tuning
architect orchestration layers and agentic workflows to ensure vendor-agnostic, autonomous problem-solving
Full-Stack Development & Prototyping: Build robust Python-based backends and scalable APIs
create interactive user interfaces (POCs) to visualize AI reasoning and gather clinical stakeholder feedback
Data & Infrastructure Integration: Integrate AI solutions with cloud data warehouses (e.g., Snowflake) and manage containerized deployments (Docker) via automated CI/CD and GitOps pipelines (GitLab, ArgoCD) on GCP
Governance, Security, & Monitoring: Engineer automated guardrails for PII/PHI masking and risk mitigation
implement observability tools to monitor model drift, hallucination rates, and token-based cost metrics (FinOps)
Safety & Interoperability: Validate clinical logic using advanced evaluation frameworks (e.g., RAGAS) and ensure seamless EHR integration through healthcare data standards like FHIR and HL7

Fulltime

Sr Staff ML Engineer - Applied AI

We are building AI-native discovery experiences across Mobility and Delivery. Se...

Location

United States , San Francisco

Salary:

267000.00 - 297000.00 USD / Year

Uber

Expiration Date

Until further notice

Requirements

Masters degree or Ph.D in Computer Science, Engineering, Mathematics
12+ years of ML experience, including significant work on large-scale deep learning systems
Demonstrated ownership of high-impact ML systems in search, recommendations, or conversational AI
Deep expertise in transformers, retrieval systems, ranking, and embedding architectures
Strong experience with PyTorch and distributed training
Proven ability to set the technical strategy for a large organization and influence product roadmaps at the executive level
Strong product intuition and ability to connect model improvements to business outcomes

Job Responsibility

Define and champion the multi-year technical vision and architecture for foundation models across Search, Recommendations, and Conversational AI
Set the architectural standard and drive system design for critical, high-leverage ML platforms across Mobility and Delivery
Lead cross-team initiatives spanning Retrieval, Ranking, Personalization, and LLM-powered assistants, resolving complex technical trade-offs across organizational boundaries
Define long-term investment areas (build vs fine-tune vs partner models) with clear business rationale and long-term viability
Provide principal-level technical leadership, mentoring Staff and Senior Staff engineers, and setting the bar for technical excellence across the entire AI organization

What we offer

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

Fulltime

Staff ML Engineer - Applied AI

Applied AI is a horizontal AI team at Uber partnering with product and platform ...

Location

India , Bangalore

Salary:

Not provided

Uber

Expiration Date

Until further notice

Requirements

10+ years of industry experience in machine learning or software engineering, with a proven record of delivering ML solutions to production
Strong knowledge of machine learning, deep learning, and exposure to generative AI techniques (e.g., transformers, LLMs, diffusion)
Experience designing and scaling ML systems or platforms, including training pipelines, serving infrastructure, and model lifecycle tooling
Fluency in ML frameworks (e.g., PyTorch, TensorFlow, JAX) and development in Python and/or scalable backend languages (e.g., Java, Go)
Excellent collaboration and communication skills with the ability to work across teams and functions

Job Responsibility

Design and implement ML-driven systems that power core Uber experiences, with a focus on scalability, reliability, and performance
Lead the technical execution of key projects involving classical ML, deep learning, and generative AI technologies (e.g., LLMs, multimodal models)
Collaborate closely with product, data science, and infrastructure teams to develop AI solutions from ideation through production deployment
Contribute to and influence the technical direction for Applied AI, particularly around system design, model architecture, and infrastructure decisions
Champion engineering best practices in ML development — including experimentation workflows, model versioning, evaluation, monitoring, and responsible AI
Provide mentorship to engineers on the team and across partner orgs to help raise the technical bar

Fulltime

Machine Learning Research Engineer, GenAI Applied ML

Lead applied ML engineering on Scale's Applied ML team, powering data infrastruc...

Location

United States , San Francisco; New York

Salary:

176000.00 - 220000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

PhD or MSc in Computer Science, Mathematics, Statistics, or related field
3+ years shipping scaled production ML systems
Demonstrated real-world impact
Mastery of PyTorch, TensorFlow, JAX, or scikit-learn
Deep expertise in agentic LLMs and multi-agent systems
Strong software engineering and microservices (AWS/GCP)
Rapid, data-driven iteration
Proficiency using AI tools to accelerate work
Strong research depth with practical bias
Excellent cross-functional communication

Job Responsibility

Build and deploy multi-agent systems for agentic reasoning validation
Develop pipelines to detect errors and scale human judgment
Combine classical ML, LLMs, and multi-agent techniques for reliability
Lead research into agent failure modes and ship fixes
Use AI tools to speed prototyping and iteration
Build data-driven evaluations and deploy rapid improvements
Integrate systems into Scale's platform

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO

Fulltime

Lead AI ML Engineer

We build breakthrough software products that power digital businesses. We are an...

Location

India , Noida

Salary:

Not provided

3Pillar Global

Expiration Date

Until further notice

Requirements

8+ years of total IT experience
At least 4+ years in AI/ML development
Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-Learn)
Experience with NLP libraries such as spaCy, Hugging Face Transformers
Solid understanding of AI/ML algorithms, data preprocessing, and model evaluation techniques
Hands-on experience with Generative AI, LLMs, and Agentic AI
Working knowledge of MLOps tools and CI/CD pipelines for AI model deployment
Familiarity with computer vision frameworks (OpenCV)
Excellent problem-solving and communication skills
Ability to lead and mentor junior engineers

Job Responsibility

Lead the design and implementation of AI/ML models and solutions for complex business problems
Work on NLP, LLMs, and Generative AI to build intelligent systems and conversational agents
Develop and optimize models using Python, TensorFlow, PyTorch, and Scikit-Learn
Apply deep learning and transformer-based architectures (e.g., BERT, GPT) for NLP and vision tasks
Implement computer vision solutions using OpenCV and related tools
Collaborate with cross-functional teams to integrate AI models into production systems
Apply MLOps best practices and manage CI/CD pipelines for model deployment
Stay updated with the latest AI research, LLM, and Agentic AI trends, and drive innovation across teams

Fulltime

Staff Machine Learning Engineer, Money (Founding ML Lead)

The Money org at DoorDash builds the financial products and platforms that power...

Location

United States , San Francisco; Los Angeles

Salary:

137100.00 - 299300.00 USD / Year

DoorDash

Expiration Date

Until further notice

Requirements

8+ years of industry experience building and deploying production-scale ML systems
Direct experience with credit, lending, or cash advance underwriting, including risk modeling and decisioning
Strong foundation in statistics, probability, and machine learning, and know how to apply them to noisy, real-world financial data
Fluent in Python (and/or Java, Scala, or C++) and experienced with ML frameworks such as XGBoost, PyTorch, TensorFlow, or similar
Designed and operated ML systems in production, including monitoring, retraining, and model governance
Can lead complex technical projects end-to-end, influencing stakeholders across multiple orgs
Communicate clearly and effectively with technical and non-technical audiences
Excited about building something new and operating with ambiguity at high ownership

Job Responsibility

Lead the design, development, and deployment of production-grade ML systems that drive financial decisioning across DoorDash’s Money ecosystem
Own ML-driven underwriting models for merchant cash advances, partnering closely with Product, Risk, Data Science, and Engineering to improve approval rates, loss performance, and capital efficiency
Broaden scope to support additional initiatives such as banking products, payouts, and consumer payments optimization
Set best practices for model development, deployment, monitoring, and governance across the Money org
Influence architecture, strategy, and execution beyond a single team
Help shape the future of Money ML at DoorDash, with the opportunity to mentor others and grow a team over time

What we offer

401(k) plan with employer matching
16 weeks of paid parental leave
Wellness benefits
Commuter benefits match
Paid time off
Paid sick leave
Medical, dental, and vision benefits
11 paid holidays
Disability and basic life insurance
Family-forming assistance

Senior ML Engineer

We are the global test and automation specialists, powering next-generation tech...

Location

United States , North Reading

Salary:

158600.00 - 253700.00 USD / Year

Teradyne

Expiration Date

Until further notice

Requirements

5+ years of experience in machine learning, applied AI, or related fields
Hands-on experience fine-tuning large language models
Experience with reinforcement learning (e.g., policy gradients, PPO, actor-critic methods)
Experience designing reward models or evaluation systems
Strong software engineering skills (Python, distributed systems familiarity)
Experience building production ML systems (MLOps, monitoring, deployment)
Ability to work cross-functionally with product, software, and hardware teams
Strong communication skills
comfortable engaging directly with customers & stakeholders
Computer vision skills in manufacturing inspection: defect detection, etc.

Job Responsibility

Own the technical direction and quality of all ML model development across Teradyne's AI initiatives, setting and enforcing engineering standards across the team
Lead end-to-end development of production ML systems: data ingestion and feature engineering, model architecture design, training pipelines, evaluation frameworks, and deployment
Design and implement novel ML approaches tailored to Teradyne's unique data domain including time-series parametric test data (STDF/TEMS), wafer map analysis, etc.
Drive applied research and model innovation, explore and evaluate new architectures, algorithms, and training methodologies, and translate promising approaches into production systems
Develop and maintain rigorous model evaluation frameworks, including validation methodologies, risk quantification, and production monitoring strategies
Lead technical design reviews
serve as final arbiter of ML architecture and modeling decisions for the team
Build and maintain production ML systems with a strong focus on reliability, scalability, and performance in Teradyne's ATE and manufacturing environments
Partner directly with customers and application engineers to understand real-world debug workflows and translate them into ML solutions
Mentor and develop junior ML engineers

What we offer

medical
dental
vision
Flexible Spending Accounts
retirement savings plans
life and disability insurance
paid vacation & holidays
tuition assistance programs

Fulltime

ML Engineer Senior - GenAI Solutions

As a Senior Machine Learning Engineer at NTT DATA, you will work alongside exper...

Location

Italy , Milano

Salary:

Not provided

NTT DATA

Expiration Date

Until further notice

Requirements

At least 5 years of production experience working in Data Science or Software Engineering
Deep knowledge of math, probability, statistics and algorithms
At least 6/12 months of experience in Generative AI deployment and underlying architecture handling
Vector Database knowledge is well appreciated
Understanding of data structures, data modeling and software architecture
Fluent in a at least two mainstream programming language (Python, Scala, Java, C++)
Experience in building an infrastructure for technical users, such as Data Scientist, ML practitioners or data consumers/producers
Strong knowledge of Spark, Databricks is a strong plus
Experience developing/deploying ML solutions in one of the public cloud platforms and on a Cross-cloud base, Snowflake knowledge is a plus
Deep knowledge with machine learning frameworks (such as Keras or PyTorch)

Job Responsibility

Apply hands-on Generative AI capabilities, preferably on Azure/GCP and on-premise GenAI architectures and MLOps
Leverage a strong mathematical background
Work on classification, information retrieval, clustering and optimization problems
Establish scalable, efficient and automated processes for large-scale data analysis
Contribute to model development, model validation and model implementation
Identify business opportunities
Design and create new data pipelines from scratch, from experiments to production deployment
Manage multiple projects
Lead ML Engineers
Connect with stakeholders

Fulltime

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Lead Applied ML Engineer

Job Description

Job Responsibility

Requirements

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Lead Applied ML Engineer

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