# AI/ML Platform Software Engineer
## About the Role
Vida is seeking an AI/ML Platform Software Engineer to help rebuild and scale its data platform while creating the foundation for future AI and machine learning capabilities.
This is a hands-on, generalist engineering role spanning backend software, infrastructure, data engineering, DevOps, reliability, security, and AI/ML systems. You will contribute to data ingestion and transformation, canonical data models, entity resolution, event infrastructure, configuration as code, API and serving layers, and production reliability.
You will also help establish the platform foundation for model serving, inference infrastructure, feature and training data pipelines, model lifecycle management, LLM applications, grounding, and guardrails, while protecting sensitive patient data and supporting requirements associated with regulated healthcare environments.
Employment Type: Full-Time
Work Arrangement: Remote within the United States
Compensation: $250,000–$275,000 per year
## Key Responsibilities
### Data & Platform Engineering
Contribute across the data platform, including:
Data ingestion
Data transformation
Canonical data models
Entity resolution
Event backbone
Configuration as code
API and serving layers
Help build platform capabilities that support self-service analytics and future AI/ML use cases.
### DevOps & Reliability
Strengthen CI/CD and infrastructure as code practices.
Support environment management and production reliability.
Implement and improve observability across metrics, logs, traces, and service level objectives (SLOs).
Establish scalable patterns for production infrastructure and platform operations.
### Security & Compliance
Partner with Security and IT on data classification and access-control enforcement.
Support secrets and key management.
Implement controls aligned with HIPAA and HITRUST requirements.
Keep protected health information within controlled boundaries.
Favor in-VPC or local inference where required.
Ensure AI/ML integrations do not create paths for sensitive data leakage.
### AI/ML Platform
Build foundational capabilities for the AI/ML platform, including:
Model serving
Inference gateways
Feature and training data pipelines
Model registries
Model evaluation
Model deployment
Model monitoring
LLM integration
Grounding and guardrails
Work with platform, security, and DevOps team leads to increase AI/ML fluency and establish reusable engineering patterns.
Contribute hands-on across the technology stack and take on additional responsibilities as needed.
## Required Qualifications
Bachelor's degree or higher.
7–10+ years of experience building and scaling production-grade machine learning or AI systems.
Demonstrated experience as a generalist Software Engineer across backend engineering, infrastructure, and data.
Substantial hands-on experience building and deploying AI/ML systems in production.
Experience with MLOps, model serving, inference infrastructure, feature pipelines, data pipelines, and integrating models into production applications.
Strong cloud experience, ideally with Google Cloud, including technologies such as Vertex AI, BigQuery, Cloud Run, Pub/Sub, Cloud SQL, or GKE; AWS or Azure equivalents are also welcome.
DevOps and reliability experience with CI/CD, Terraform or similar infrastructure-as-code tools, containers, Kubernetes, and production observability.
Solid data engineering experience, including SQL, dbt or similar pipeline tooling, and data warehousing.
Security-minded approach and comfort working with sensitive data in regulated environments.
Experience with LLM applications, including retrieval, evaluation, guardrails, and knowledge graphs.
Comfortable working with ambiguity and moving quickly from problem definition to production solutions.
Ability to work deeply in one technical area while moving fluidly across the broader technology stack.
## Preferred Qualifications
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.
Familiarity with HIPAA or HITRUST.
## Skills & Competencies
AI/ML Engineering
Machine Learning Systems
MLOps
Software Engineering
Backend Engineering
Data Engineering
Data Platforms
AI/ML Platforms
Model Serving
Inference Infrastructure
LLM Applications
Retrieval
Model Evaluation
AI Guardrails
Knowledge Graphs
Feature Pipelines
Data Pipelines
Model Lifecycle Management
SQL dbt
Data Warehousing
Google Cloud
Vertex AI
BigQuery
Cloud Run
Pub/Sub
Cloud SQL
GKE
AWS
Azure
CI/CD
Terraform
Kubernetes
Docker
Observability
Infrastructure as Code
API Development
Data Security
HIPAA
HITRUST
Privacy-Enhancing Technologies
## Education & Experience
Bachelor's degree at a minimum.
7–10+ years of experience building and scaling production-grade AI/ML systems.
Production experience across AI/ML engineering, backend software, infrastructure, and data is required.
## Work Arrangement & Schedule
Remote position within the United States.
Employees must reside in and be able to work from the United States.
International work is prohibited.
Employment is available only in states where Vida is authorized to do business.
Job postings remain open through the end of the year until filled.
## Compensation & Benefits
Annual salary: $250,000–$275,000.
## Compliance / Additional Information
Applicants must be authorized to work in the United States. Vida is unable to sponsor work visas for any position.
Vida operates in many, but not all, U.S. states. Eligibility depends on the employee's location and Vida's authorization to conduct business in that state.
Vida is an Equal Employment Opportunity and Affirmative Action employer.
Vida is committed to reasonable accommodations for qualified individuals with disabilities and disabled veterans during the application process.
* Vida may use AI tools to support portions of its hiring process, including application and resume review, response analysis, and identification of potential inconsistencies or verification signals. These tools assist the recruiting team; final hiring decisions are made by humans.