AI/ML Engineer - Remote

About the Role

Vida Health is seeking a **Principal AI/ML Engineering Lead** to help build and scale the data and AI/ML platform powering its healthcare technology.

Vida is rebuilding its data platform with a strong focus on **patient data protection, data governance, secure data sharing, self-serve analytics, and production AI/ML capabilities**. In this role, you will take a hands-on engineering approach across AI/ML, software, data, infrastructure, DevOps, and security while establishing scalable patterns for future platform development.

You will work closely with platform, security, IT, and DevOps teams to build production-grade systems that enable AI/ML capabilities while maintaining strong controls around **protected health information (PHI)** and regulated healthcare data.

Key Responsibilities

Data & Platform Engineering

* Contribute across the platform, including:

* Data ingestion

* Data transformation

* Canonical data models

* Entity resolution

* Event backbone

* Configuration as code

* API and serving layers

* Help build scalable and reliable infrastructure for data and AI/ML workloads.

DevOps & Reliability

* Strengthen **DevOps and production reliability** across the platform.

* Build and maintain **CI/CD pipelines** and infrastructure as code.

* Support environment management and deployment workflows.

* Implement production observability across **metrics, logs, traces, and service-level objectives (SLOs)**.

* Apply containerization and Kubernetes-based infrastructure where appropriate.

Security & Healthcare Data Protection

* Partner with **Security and IT teams** on platform security and data protection.

* Support data classification and access-control enforcement.

* Implement secure secrets and key management practices.

* Maintain controls aligned with **HIPAA and HITRUST** requirements.

* Ensure protected health information remains within controlled boundaries.

* Favor **in-VPC or local inference** where required to protect sensitive data.

* Ensure AI/ML implementations do not introduce paths for healthcare or patient data leakage.

AI/ML Platform Engineering

* Build the foundation for the organization’s **AI/ML platform**.

* Develop model serving and **inference gateway** capabilities.

* Build feature and training data pipelines.

* Support the complete model lifecycle, including:

* Model registry

* Model evaluation

* Model deployment

* Model monitoring

* Integrate **LLM applications** with appropriate grounding and guardrails.

* Support retrieval, evaluation, and knowledge-graph-based AI/ML applications.

* Integrate machine learning models into production applications.

Technical Leadership

* Work alongside existing team leads to increase **AI/ML fluency** across platform, security, and DevOps teams.

* Establish engineering patterns and technical approaches that other teams can build upon.

* Work effectively across the technology stack while maintaining hands-on ownership.

* Take on additional responsibilities as needed.

Required Qualifications

* **Bachelor’s degree** or higher.

* **7–10+ years of experience** building and scaling production-grade **AI/ML or machine learning systems**.

* Demonstrated experience as a **generalist Software Engineer** working across backend engineering, infrastructure, and data.

* Significant hands-on experience building and deploying **AI/ML systems in production**.

* Experience with:

* **MLOps**

* Model serving and inference infrastructure

* Feature pipelines

* Data pipelines

* Production model integration

* Strong cloud engineering experience, ideally with **Google Cloud Platform (GCP)**.

* Experience with GCP services such as:

* **Vertex AI**

* **BigQuery**

* **Cloud Run**

* **Pub/Sub**

* **Cloud SQL**

* **GKE**

* AWS or Azure cloud experience is also welcome.

* Strong **DevOps and reliability engineering** skills.

* Experience with:

* **CI/CD**

* **Terraform** or similar infrastructure-as-code tools

* Containers

* Kubernetes

* Production observability

* Strong **data engineering** capabilities, including:

* SQL

* Data pipeline tooling such as **dbt**

* Data warehousing

* Security-focused mindset and ability to work with **sensitive data in regulated environments**.

* Ex…