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Vida Health is a virtual obesity care provider combining evidence-based treatment with advanced technology, serving Fortune 100 companies and major national payers. The company is rebuilding its data platform with a strong emphasis on patient data protection, data governance, and self-serve analytics. AI/ML are central to the platform's future success.
In this Principal AI/ML Engineering Lead role, you will take a hands-on approach to ensuring the platform is architected to safely and quickly add strategic AI/ML capabilities. You'll contribute across the full platform build, including data ingestion, transformation, canonical data modeling, entity resolution, event backbone, configuration as code, and API/serving layers.
Key responsibilities include:
- Strengthening DevOps and reliability through CI/CD, infrastructure as code, environment management, and comprehensive observability (metrics, logs, traces, SLOs)
- Partnering with Security and IT teams on data classification, access control enforcement, secrets management, and HIPAA/HITRUST compliance
- Laying groundwork for the AI/ML platform: model serving, inference gateway, feature and training data pipelines, model lifecycle management (registry, evaluation, deployment, monitoring), and LLM integration with grounding and guardrails
- Ensuring protected health information remains within controlled boundaries, favoring in-VPC or local inference where required
- Working with existing team leads to raise AI/ML fluency across platform, security, and DevOps teams while establishing patterns for others to build on
This is a generalist role requiring depth across backend, infrastructure, data, and ML systems, with a security-first mindset in a regulated healthcare environment.
REQUIREMENTS:
- Bachelor's Degree minimum
- 7-10+ years building and scaling production-grade machine learning or AI systems
- Demonstrated experience as a generalist Software Engineer across backend, infrastructure, and data
- Substantial hands-on experience building and deploying AI/ML systems in production, including MLOps, model serving, inference infrastructure, feature and data pipelines, and integrating models into real applications
- Strong cloud experience, ideally Google Cloud (Vertex AI, BigQuery, Cloud Run, Pub/Sub, Cloud SQL, GKE); AWS or Azure equivalents also welcome
- DevOps and reliability skills: CI/CD, Terraform or similar, containers, Kubernetes, and production observability
- Solid data engineering: SQL, pipeline tooling (e.g., dbt), data warehousing
- Security mindset and comfort working in regulated environments with sensitive data; HIPAA or HITRUST familiarity is a plus
- Experience with LLM applications, including retrieval, evaluation, guardrails, and knowledge graphs
- Comfortable with ambiguity and bias for shipping; ability to go deep in one area while moving fluidly across the stack
PREFERRED:
- Experience with entity resolution or master data management
- Healthcare or other regulated data domain experience
- GraphQL or federated serving layer experience
- Privacy-enhancing technologies experience
