Principal AI/ML Engineering Lead
Remote United States Full-time Senior AI Engineering
At a glance
Senior AI Engineering role at Vida Health. Remote, United States · full-time · $250,000–$275,000 base.
SalaryStated by Vida Health
$250,000 – $275,000
Base salary for this role, in USD per year, as published in the posting.
RoleAI Engineering
SenioritySenior
LocationUnited States
WorkplaceRemote
EmploymentFull-time
Base pay$250,000–$275,000
PostedAug 31, 2026 · 7w ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Build the data platform across ingestion, transformation, shared data models, event flows, configuration and serving layers
- Improve delivery and reliability with automated releases, infrastructure management, environment controls and production observability
- Set up model deployment, inference, training data flows and lifecycle controls, including evaluation and monitoring
- Protect sensitive health data through access rules, credential controls and bounded inference paths
- Work with platform, security and DevOps leads to strengthen AI/ML skills and establish reusable engineering patterns
What you bring
- At least seven years building production AI or machine-learning systems, with experience scaling them
- A bachelor's degree or higher, plus broad software engineering work across backend, infrastructure and data
- Hands-on production experience with model operations, serving infrastructure, data pipelines and application integration
- Cloud, DevOps and data engineering skills, including CI/CD, infrastructure tooling, containers, Kubernetes, observability, SQL and warehousing
- A security-conscious approach to sensitive data, with experience applying LLM retrieval, evaluation or guardrails
Who this fits
This is a hands-on senior engineering role for someone who can lead AI/ML platform work while contributing across software, infrastructure and data. You must be authorized to work in the U.S. and reside in, or be able to work from, a state where Vida is registered; visa sponsorship and international work are unavailable.
From the employer
ABOUT US
At Vida, we help people get better- and we're helping the healthcare system get better, too.
Vida is a virtual, personalized obesity care provider that uses evidence-based treatment to help patients manage obesity and related conditions like diabetes, high blood pressure, anxiety and depression. Vida's team of Obesity Medicine-Certified Physicians, Registered Dietitians, Expert Coaches and Licensed Therapists takes a whole-person approach to care, helping people lose weight, reduce stress and improve their overall health.
By combining advanced technology with top-notch healthcare providers, Vida is breaking down the barriers that have historically kept people from getting the best care. It's trusted by Fortune 100 companies, major national payers and large providers to enable their employees to live their healthiest lives.
Vida's technology organization is rebuilding its data platform, with a strong emphasis on protecting patient data, governing how data can be shared, and enabling self-serve analytics. AI/ML are central to the future success of the product and business. This role takes a hands-on approach to ensuring the platform is built so these strategic capabilities can be added safely and quickly.
Vida's technology organization is rebuilding its data platform, with a strong emphasis on protecting patient data, governing how data can be shared, and enabling self-serve analytics. AI/ML are central to the future success of the product and business. This role takes a hands-on approach to ensuring the platform is built so these strategic capabilities can be added safely and quickly.
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.