MLOps Engineer (Scientific Platforms / Data Foundry)

Quick Facts

• Role: MLOps Engineer (Scientific Platforms / Data Foundry)

Description

You will operationalize Data Foundry’s scientific tools and analytical methods by building ML deployment pipelines, model serving infrastructure, API layers, and observability guardrails. The work sits between analytical methods and data infrastructure to ensure outputs are analytics-ready, well-monitored, and exposed through APIs with response-time guarantees and robust error handling for both human scientists and autonomous AI agents. You’ll productionize predictive/analytical methods and support synchronous and asynchronous workloads.

Responsibilities

• Build and maintain end-to-end ML deployment pipelines (experiment tracking, model versioning, containerized serving, automated retraining triggers)

• Develop model registry and feature engineering pipelines for computational scientists

• Implement monitoring and alerting for data pipelines, APIs, models, and agentic (LLMOps) systems; build dashboards/metrics

• Set up structured logging and tracing for debugging and performance optimization

• Deploy predictive/analytical methods with versioning, structured error handling, and response-time guarantees

• Create serving infrastructure for synchronous and asynchronous workloads

• Define API contracts, documentation standards, and testing frameworks for tool robustness and consumability

• Build and operate cloud-native model serving using containers, Kubernetes, and infrastructure-as-code

• Create CI/CD pipelines for ML models (validation, A/B testing, canary releases, rollback)

• Integrate model serving with versioned training/inference data pipelines

• Collaborate with Frontier AI and Tech teams on REST APIs and MCP-compatible endpoints and agent invocation

• Define API performance requirements (latency/throughput) and graceful degradation

• Ensure deployed models include uncertainty quantification and confidence metrics

Requirements

• B.S. or M.S. in Computer Science, Data Science, Machine Learning, Bioinformatics, Computational Biology, or related field

• 3+ years of experience in MLOps, ML engineering, or scientific platform development

• Authorization to work in the United States on a full-time basis (no visa sponsorship)

Benefits

• Eligible for company bonus (based on company and individual performance)

• Comprehensive benefits: 401(k), pension, vacation, medical/dental/vision/prescription coverage, flexible benefits, life insurance, and well-being benefits (e.g., employee assistance, fitness benefits)

• Equal opportunity and accommodations available during the application process