Lead the hands-on development of Vida’s secure data and AI/ML platform, including ingestion, data modeling, MLOps, model serving, feature pipelines, LLM integrations, DevOps, observability, and infrastructure. Partner with security and IT to protect sensitive health data and meet HIPAA/HITRUST-aligned controls while establishing scalable engineering patterns across the organization.
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.
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.
Vida is proud to be an Equal Employment Opportunity and Affirmative Action employer.
Diversity is more than a commitment at Vida—it is the foundation of what we do. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, religion, gender, gender identity or expression, sexual orientation, marital status, national origin, genetics, disability, age, or Veteran status. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.
We seek to recruit, develop and retain the most talented people from a diverse candidate pool. We don’t just accept differences — we celebrate them, we support them, and we thrive on them for the benefit of our employees, our platform and those we serve. Vida is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures.
We do not accept unsolicited assistance from any headhunters or recruitment firms for any of our job openings. All resumes or profiles submitted by search firms to any employee at Vida in any form without a valid, signed search agreement in place for the specific position will be deemed the sole property of Vida. No fee will be paid in the event the candidate is hired by Vida as a result of the unsolicited referral.
**Vida is authorized to do business in many, but not all, states. If you are not located in or able to work from a state where Vida is registered, you will not be eligible for employment. Please speak with your recruiter to learn more about where Vida is registered.
Please note: Applicants must be authorized to work in the U.S. as Vida is unable to sponsor work visas for any position.
All Vida Employees must reside in/be able to work from the U.S.- international work is prohibited. Job postings at Vida will remain open through end of year, until filled.
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