Engineer - MLOps & Scientific Platforms - Data Foundry
Eli Lilly and Company
8 days ago
Remote friendly (San Francisco, CA)
United States
IT
Engineer – MLOps & Scientific Platforms (Data Foundry)
Responsibilities:
- Build/maintain end-to-end ML deployment pipelines (experiment tracking, model versioning, containerized model serving, automated retraining).
- Create model registry + feature engineering pipelines for computational scientists.
- Implement monitoring/alerting (data pipelines, APIs, ML models, agentic/LLMOps systems) and dashboards for latency, tokens, prediction quality, and system health.
- Establish structured logging/tracing for debugging and performance.
- Deploy Methods4Insight analytical/predictive methods with versioning, structured error handling, and response-time guarantees; productionize with Tech@Lilly.
- Build serving infrastructure for synchronous and asynchronous workloads; define API contracts, documentation standards, and testing.
- Build/operate cloud-native model serving (containers, Kubernetes, IaC) and CI/CD (validation, A/B, canary, rollback).
- Partner with Frontier AI/Tech@Lilly to expose tools via REST APIs/MCP-compatible endpoints; collaborate on latency/throughput and uncertainty quantification.
Basic Requirements:
- BS/MS in CS, Data Science, ML, Bioinformatics, Computational Biology, or related.
- 3+ years in MLOps/ML engineering/scientific platform development.
- Work authorization for the US (visa sponsorship not provided).
Preferred Qualifications (selected):
- Pharmaceutical/biotech experience; strong Python; production model serving + monitoring.
- AWS/Azure/GCP, Kubernetes, CI/CD; experience operationalizing scientific models; drift/retraining.
- API gateway/event-driven/service mesh; feature stores/DVC; AI agent frameworks; GPU/CUDA/HPC container familiarity.
Compensation & Benefits:
- $66,000–$165,000 anticipated wage; bonus eligibility; 401(k), medical/dental/vision, flexible benefits, life insurance, time off/leave, well-being benefits.
Responsibilities:
- Build/maintain end-to-end ML deployment pipelines (experiment tracking, model versioning, containerized model serving, automated retraining).
- Create model registry + feature engineering pipelines for computational scientists.
- Implement monitoring/alerting (data pipelines, APIs, ML models, agentic/LLMOps systems) and dashboards for latency, tokens, prediction quality, and system health.
- Establish structured logging/tracing for debugging and performance.
- Deploy Methods4Insight analytical/predictive methods with versioning, structured error handling, and response-time guarantees; productionize with Tech@Lilly.
- Build serving infrastructure for synchronous and asynchronous workloads; define API contracts, documentation standards, and testing.
- Build/operate cloud-native model serving (containers, Kubernetes, IaC) and CI/CD (validation, A/B, canary, rollback).
- Partner with Frontier AI/Tech@Lilly to expose tools via REST APIs/MCP-compatible endpoints; collaborate on latency/throughput and uncertainty quantification.
Basic Requirements:
- BS/MS in CS, Data Science, ML, Bioinformatics, Computational Biology, or related.
- 3+ years in MLOps/ML engineering/scientific platform development.
- Work authorization for the US (visa sponsorship not provided).
Preferred Qualifications (selected):
- Pharmaceutical/biotech experience; strong Python; production model serving + monitoring.
- AWS/Azure/GCP, Kubernetes, CI/CD; experience operationalizing scientific models; drift/retraining.
- API gateway/event-driven/service mesh; feature stores/DVC; AI agent frameworks; GPU/CUDA/HPC container familiarity.
Compensation & Benefits:
- $66,000–$165,000 anticipated wage; bonus eligibility; 401(k), medical/dental/vision, flexible benefits, life insurance, time off/leave, well-being benefits.