Principal AI/ML Engineering Lead
Responsibilities:
- Contribute across the platform build: data ingestion, transformation, a canonical data model and entity resolution, an event backbone, configuration as code, and API and serving layers.
- Strengthen DevOps and reliability: CI/CD, infrastructure as code, environment management, and observability across metrics, logs, traces, and service level objectives.
- Partner with Security and IT: data classification and access control enforcement, secrets and key management, and controls aligned with HIPAA and HITRUST.
- Lay the groundwork for the AI/ML platform: model serving and an inference gateway, feature and training data pipelines, model lifecycle (registry, evaluation, deployment, monitoring), and LLM integration with grounding and guardrails.
- Keep protected health information inside controlled boundaries, favor in-VPC or local inference where required, and make sure that adding ML never opens a path for data to leak.
- Work with existing team leads to continue raising the AI and ML fluency of the platform, security, and DevOps teams as you work alongside them, and set the patterns others will build on.
- Additional responsibilities as needed.
Qualifications:
- Bachelor's Degree at a minimum.
- 7-10+ years of experience in 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 and inference infrastructure, feature and data pipelines, and integrating models into real applications.
- Strong cloud experience, ideally on Google Cloud (Vertex AI, BigQuery, Cloud Run, Pub/Sub, Cloud SQL, GKE), with AWS or Azure equivalents also welcome.
- DevOps and reliability skills: CI/CD, Terraform or a similar tool, containers and Kubernetes, and production observability.
- Solid data engineering: SQL, pipeline tooling such as dbt, and data warehousing.
- A security mindset and comfort working in a regulated environment with sensitive data. Familiarity with HIPAA or HITRUST is a plus.
- Experience with LLM applications, including retrieval, evaluation, guardrails, and knowledge graphs.
- Comfortable with ambiguity and a bias for shipping. You can go deep in one area and still move fluidly across the stack.
Preferred:
- Experience with entity resolution or master data management.
- Experience in healthcare or another regulated data domain.
- Experience with GraphQL or a federated serving layer.
- Experience with privacy enhancing technologies.
Skills
As published by lever · 8 questions · 2 written answers
Basics
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, Twitter URL, GitHub URL, Portfolio URL, Other website
Short answers (3)
- What US city and state do you reside in?
- What is your desired compensation?
- If referred, please let us know the name of who referred you so they can get credit! optional
Pick from a list (3)
- Do you reside in DE, ND, VT or WV? Vida is not authorized to employ in these states.
- Work authorization
- Were you referred to this role?
Written answers (2)
- Describe an ML system you have built or have assisted building.
- How do you approach stakeholder management? Please describe your process.