MLOps & Scientific Platforms Engineer (Data Foundry)

Quick Facts

• Role: Engineer focused on MLOps and scientific platform engineering for Data Foundry

• Focus: Operationalize Data Foundry scientific tools and analytical methods into actionable prototypes

Description

Build ML deployment pipelines, model serving infrastructure, API layers, and observability guardrails so scientific discovery methods and tools are reliable, scalable, and consumable by discovery scientists and autonomous AI agents. Partner across analytical methods and agile data infrastructure to ensure Data Foundry tools are analytics-ready, well-monitored, and exposed through APIs with response-time guarantees and robust error handling.

Responsibilities

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

• Create model registry infrastructure and feature engineering pipelines

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

• Establish structured logging and tracing for debugging and performance optimization

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

• Support synchronous (interactive) and asynchronous (batch/agent-invoked) workloads

• Define API contracts, documentation standards, and testing frameworks to make tools robust for external teams

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

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

• Integrate model serving with Data Foundry data pipelines for properly formatted, versioned training and inference data

• Collaborate with Frontier AI and Tech teams to expose tools via REST APIs and MCP-compatible endpoints; address 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

• Authorized to work in the United States full-time (no visa sponsorship)

Benefits

• Eligibility for a 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 (EAP, fitness benefits, clubs/activities)

Compensation

Anticipated wage: $66,000 - $165,000 (depends on education, experience, skills, and geographic location).