Lead ML/AI Engineer

United StatesRemoteFull-timelead

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Own the decisioning and feedback layer connecting model scores to actions, including action selection and ranking, holdout and experiment assignment, outcome capture, production ROI calculations,

Technical Tools

Machine Learning EngineerData

Take a leading role in building the production infrastructure that turns machine learning predictions into measurable actions and outcomes across healthcare markets. You will own the engineering behind a real-time decision platform, connecting models, care teams, payer systems, and feedback loops. Combining AWS-native services with a Databricks lakehouse, you will establish scalable, secure, and cost-efficient systems that operate under strict protected health information (PHI) controls. You will define technical standards for decisioning, streaming, MLOps, and generative AI applications while remaining hands-on with critical components. Working closely with data science, engineering, medical economics, clinical, and operations teams, you will ensure that models are reliable, measurable, and ready for production. This is a senior individual contributor position with significant technical ownership and influence over the platform's long-term architecture.

As Lead ML/AI Engineer, you will own the engineering foundations that connect model outputs to real-world decisions, ensure reliable feedback and measurement, and establish production standards across the ML/AI platform. Your responsibilities will include:

Own the decisioning and feedback layer connecting model scores to actions, including action selection and ranking, holdout and experiment assignment, outcome capture, production ROI calculations, and pipelines measuring the impact of actions and channels.
Lead the engineering implementation, auditability, and reproducibility of production savings calculations in collaboration with medical economics and data science teams.
Design event-driven, real-time integrations between model outputs and operational systems, including streaming pipelines, versioned schemas, data contracts, replay mechanisms, event ordering, idempotency, and failure handling.
Define and maintain integration contracts with partner teams to ensure reliable data exchange and system interoperability.
Establish cost-to-serve standards by defining compute, streaming, inference, and LLM spending budgets at the model and decision levels, making cost a release criterion alongside latency and accuracy.
Set production standards for ML models, including CI/CD, model registries, alerting, drift monitoring, coverage, and calibration across different cohorts, markets, and payers.
Build and maintain model promotion gates to ensure that models and ranking systems meet production requirements before deployment.
Own the target architecture and technical roadmap for the AWS-native infrastructure and Databricks lakehouse, including infrastructure as code, IAM, secure data handling, containerized inference, and managed foundation-model services under PHI controls.
Define technical direction and production standards for generative AI applications, including LLM pipelines and agentic workflows, with appropriate evaluation, retrieval, guardrails, and cost controls.
Collaborate with data scientists on modeling and measurement approaches, medical economics teams on savings methodology, and engineering teams on shared pipelines, interfaces, and contracts.
Establish engineering standards covering code reviews, automated testing, reproducibility, documentation, and release discipline.
Lead architecture and design reviews, document technical decisions, and mentor engineers and data scientists on system design and production engineering practices.
Represent the team's technical direction and recommendations to R&D leadership, finance, clinical, and operations stakeholders.

Requirements

~2 min read

The ideal candidate is an experienced ML/AI engineering leader who has built and operated production systems that influence real-world decisions and measure their impact. You should combine strong software and infrastructure expertise with technical leadership, sound engineering judgment, and the ability to collaborate across multidisciplinary teams.

Required Qualifications:

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience.
At least 8 years of experience in ML engineering, MLOps, or software engineering, including 4+ years operating machine learning systems in production.
At least 2 years of experience serving as the technical lead or architect of a production ML or decisioning system.
Demonstrated experience building or operating prescriptive production systems, such as recommendation, decisioning, prioritization, or experimentation platforms that allocate constrained resources against measurable outcomes.
Production experience with AWS data and machine learning services, as well as infrastructure-as-code tools such as Terraform, CDK, or CloudFormation.
Production experience with Databricks, PySpark, Delta Lake, MLflow, or equivalent technologies.
Hands-on expertise designing and operating event-driven or streaming systems using technologies such as Kinesis, Kafka, or equivalent, including schema or contract management.
Strong Python and SQL skills, with practical experience operating production ML workloads such as training pipelines, model serving, feature systems, or ML platform tooling.
Experience implementing CI/CD for ML workloads and monitoring models and pipelines, including calibration across heterogeneous populations.
Proven ability to establish engineering standards, improve team practices through code reviews, and mentor technical colleagues.
Willingness and ability to travel up to 10% for onsite meetings, team collaboration, and company events.

Preferred Qualifications:

Experience building and evaluating machine learning models using scikit-learn, XGBoost, or PyTorch.
Exposure to healthcare economics or value-based care, including savings methodologies, attribution, and payer contract constraints.
Experience with dbt and lakehouse data modeling.
Familiarity with healthcare data, including claims, pharmacy, eligibility, prior authorization, and electronic health records (EHR), as well as PHI handling requirements.
Experience with uplift modeling, bandit algorithms, sequential decisioning, or holdout-based measurement in production.
Experience with containerized model serving and staged or canary deployment strategies.
Familiarity with Amazon Bedrock or other managed foundation-model platforms operating under a HIPAA Business Associate Agreement (BAA).
Experience with agentic frameworks and production retrieval systems.
Demonstrated experience building or operating LLM-based applications, including evaluation frameworks and guardrails.