AI/ML Engineer (Remote Work)

# 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.