Job description

Datavant is the data collaboration platform trusted for healthcare. Guided by our mission to make the world’s health data secure, accessible and actionable, we provide critical data solutions for organizations across the healthcare ecosystem - including providers, health plans, researchers, and life sciences companies. From fulfilling a single patient’s request for their medical records to powering the AI revolution in healthcare, Datavanters are building the future of how data is connected and used to improve health. By joining Datavant today, you’re stepping onto a driven and highly collaborative team that is passionate about creating transformative change in healthcare. What We’re Looking For We are looking for a Staff ML Platform Engineer to join our Data & ML Platform organization on the ML Platform team. We own the paved road that takes a model from notebook to production without rebuilding it each time: training and inference across SageMaker and Databricks, self-service LLM-endpoint serving, and the pipelines behind Datavant’s clinical-AI products. Data Science owns the models and their quality; we own the pipelines, serving infrastructure, and operational guardrails that let those models run safely against regulated health data. As a Staff ML Platform Engineer, you will be a technical leader across the ML Platform: setting direction for the paved road, owning the hardest architectural problems, and moving the team from bespoke plumbing toward a coherent platform that Data Science teams can self-serve. AI fluency is a baseline expectation. You should already be using Claude Code, Cursor, Copilot, or equivalent tools as a core part of your daily engineering workflow, have opinions about how they make a team faster, and know how to apply them responsibly when PHI and other sensitive data are in scope. What You Will Do Set technical direction across ML training, serving, and observability, and be the final escalation point for the most elusive infrastructure problems (GPU capacity, Spark tuning, production incidents) Own and evolve our paved-road framework (the shared CI/CD spine, model-workflow scaffolding, and Databricks Asset Bundles) so Data Science teams can go from a config file to a production workflow without bespoke plumbing Lead architecture for LLM-endpoint serving across managed providers (Databricks, AWS, Snowflake) and self-hosted deployments, covering latency, cost, caching, evaluation, and PHI-safe routing Own the standards and tooling for MLflow, model registry, training image supply chain, and observability across training and inference Partner closely with your Data & ML Platform teammates to present a cohesive ML platform to the Data Science, App Dev, and Operations teams at Datavant Serve as a key technical input to vendor and platform selection decisions across model providers, ML tooling, and observability Mentor senior engineers on the team, provide technical guidance to platform consumers, and stay hands-on writing high-leverage code and Infrastructure-as-Code alongside your teammates What You Need to Succeed 10+ years of software engineering experience, with 3+ years designing, evolving, and operating enterprise-scale ML platforms in production Strong technical judgment under ambiguity and a track record of setting standards, influencing peers, and raising the bar across teams Hands-on production experience with Databricks and/or Amazon SageMaker, MLflow (or an equivalent tracking + registry system), and at least one core ML framework (PyTorch, TensorFlow, or similar) Fluency in Java (or a JVM equivalent) and Python, with real depth in Apache Spark for large-scale data and distributed compute Real depth in AWS: networking, IAM, GPU compute, and the storage and messaging services this role touches, with the judgment to know what to reach for and when Fluency with Terraform, containers, Kubernetes, and GitHub-based CI/CD for ML workloads Direct experience serving LLMs in production, including cost management, evaluation harnesses, and safe handling of sensitive prompts and outputs AI-native working style: daily use of Claude Code, Cursor, Copilot, or equivalent, with views on how they make a team faster Clear written and verbal communication, especially in async, remote settings What Helps You Stand Out Prior technical leadership on a healthcare or regulated-industry ML platform (HIPAA, HITRUST, SOC 2, or equivalent) Direct experience with Databricks Asset Bundles, Unity Catalog, and open table formats (Iceberg, Delta) Production experience with specialized inference pipelines beyond generic model serving (e.g., document understanding, computer vision, or streaming inference) GPU capacity planning at strategic, tactical, and operational horizons Experience with real-time and event-driven inference and streaming platforms (Kafka, Kinesis) Evaluation and red-teaming experience for clinical or safety-sensitive AI Background contributing to open-source ML infrastructure or publishing on production ML systems We are committed to building a diverse team of Datavanters who are all responsible for stewarding a high-performance culture in which all Datavanters belong and thrive. We are proud to be an Equal Employment Opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status. At Datavant our total rewards strategy powers a high-growth, high-performance, health technology company that rewards our employees for transforming health care through creating industry-defining data logistics products and services. The range posted is for a given job title, which can include multiple levels. Individual rates for the same job title may differ based on their level, responsibilities, skills, and experience for a specific job. The estimated total cash compensation range for this role is: $224,000 — $280,000 USD To ensure the safety of patients and staff, many of our clients require post-offer health screenings and proof and/or completion of various vaccinations such as the flu shot, Tdap, COVID-19, etc. Any requests to be exempted from these requirements will be reviewed by Datavant Human Resources and determined on a case-by-case basis. Depending on the state in which you will be working, exemptions may be available on the basis of disability, medical contraindications to the vaccine or any of its components, pregnancy or pregnancy-related medical conditions, and/or religion. This job is not eligible for employment sponsorship. Datavant is committed to a work environment free from job discrimination. We are proud to be an Equal Employment Opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status. To learn more about our commitment, please review our EEO Commitment Statement here . Know Your Rights , explore the resources available through the EEOC for more information regarding your legal rights and protections. In addition, Datavant does not and will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay. At the end of this application, you will find a set of voluntary demographic questions. If you choose to respond, your answers will be anonymous and will help us identify areas for improvement in our recruitment process. (We can only see aggregate responses, not individual ones. In fact, we aren’t even able to see whether you’ve responded.) Responding is entirely optional and will not affect your application or hiring process in any way. Datavant is committed to working with and providing reasonable accommodations to individuals with physical and mental disabilities. If you need an accommodation while seeking employment, please request it here, by selecting the ‘Interview Accommodation Request’ category. You will need your requisition ID when submitting your request, you can find instructions for locating it here . Requests for reasonable accommodations will be reviewed on a case-by-case basis. For more information about how we collect and use your data, please review our Privacy Policy .

Requirements

Must-haves

Set technical direction across ML training, serving, and observability, and be the final escalation point for the most elusive infrastructure problems (GPU capacity, Spark tuning, production incidents)
Own and evolve our paved-road framework (the shared CI/CD spine, model-workflow scaffolding, and Databricks Asset Bundles) so Data Science teams can go from a config file to a production workflow without bespoke plumbing
Lead architecture for LLM-endpoint serving across managed providers (Databricks, AWS, Snowflake) and self-hosted deployments, covering latency, cost, caching, evaluation, and PHI-safe routing
Own the standards and tooling for MLflow, model registry, training image supply chain, and observability across training and inference
Partner closely with your Data & ML Platform teammates to present a cohesive ML platform to the Data Science, App Dev, and Operations teams at Datavant
Serve as a key technical input to vendor and platform selection decisions across model providers, ML tooling, and observability
Mentor senior engineers on the team, provide technical guidance to platform consumers, and stay hands-on writing high-leverage code and Infrastructure-as-Code alongside your teammates
What You Need to Succeed
10+ years of software engineering experience, with 3+ years designing, evolving, and operating enterprise-scale ML platforms in production
Strong technical judgment under ambiguity and a track record of setting standards, influencing peers, and raising the bar across teams
Hands-on production experience with Databricks and/or Amazon SageMaker, MLflow (or an equivalent tracking + registry system), and at least one core ML framework (PyTorch, TensorFlow, or similar)
Fluency in Java (or a JVM equivalent) and Python, with real depth in Apache Spark for large-scale data and distributed compute
Real depth in AWS: networking, IAM, GPU compute, and the storage and messaging services this role touches, with the judgment to know what to reach for and when
Fluency with Terraform, containers, Kubernetes, and GitHub-based CI/CD for ML workloads
Direct experience serving LLMs in production, including cost management, evaluation harnesses, and safe handling of sensitive prompts and outputs
AI-native working style: daily use of Claude Code, Cursor, Copilot, or equivalent, with views on how they make a team faster
Clear written and verbal communication, especially in async, remote settings
What Helps You Stand Out
Prior technical leadership on a healthcare or regulated-industry ML platform (HIPAA, HITRUST, SOC 2, or equivalent)
Direct experience with Databricks Asset Bundles, Unity Catalog, and open table formats (Iceberg, Delta)
Production experience with specialized inference pipelines beyond generic model serving (e.g., document understanding, computer vision, or streaming inference)
GPU capacity planning at strategic, tactical, and operational horizons
Experience with real-time and event-driven inference and streaming platforms (Kafka, Kinesis)
Evaluation and red-teaming experience for clinical or safety-sensitive AI

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlllmci/cdgoawsjavapythonkubernetes

Platform: greenhouse
Effort: quick

Benefits

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