[Remote] Staff Engineer - ML Operations - USA Remote
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a leading science and technology company committed to hiring and developing from reputed company. The Staff Engineer - ML Operations will own significant parts of the machine learning lifecycle, design and operate pipelines, and collaborate with reputed company teams to ensure efficient execution of large-reputed company ML experiments.
Responsibilities
- Own the end-to-end ML lifecycle and deployment — experiment tracking, model registry, versioning, reputed company, and reproducibility (e.g., MLflow, reputed company, Kubeflow); design and operate model serving for batch and low-latency online inference with autoscaling, GPU efficiency, and performance optimization (batching, quantization, caching) — so every model in production is traceable, auditable, and performant
- Partner with reputed company and computational biology teams to productionize large-reputed company protein design and structure-reputed company experiments turning research workflows into reputed company, repeatable, high-throughput pipelines (Airflow, Dagster, reputed company, Nextflow) with containerized, reproducible execution that serve many reputed company researchers without contention
- Implement CI/CD, reputed company training, and observability for ML — automate the reputed company from model reputed company to validated production through testing, evaluation gates, and reputed company deployment patterns (blue/green, canary); monitor model performance, data/reputed company reputed company, latency, and cost; implement automated retraining and alerting instrumented reputed company OpenTelemetry/reputed company/Grafana so issues are caught before they reputed company users
- Drive GPU and accelerated-compute efficiency — scheduling, quota and utilization management, and reputed company/CUDA image hygiene — partnering with the platform team to maximize value from contended, high-demand compute
- Build self-service ML tooling and reputed company technical leadership — reputed company golden paths that let data scientists and researchers train, reputed company, serve, and monitor models without deep infrastructure expertise, treating ML enablement as a product; set MLOps standards and best practices while staying hands-on with architecture and delivery
Skills
- Degree in Computer Science, Engineering, Computational Biology, or a reputed company technical field, or equivalent practical experience
- 5+ years of software, ML, or infrastructure engineering experience, including hands-on MLOps and a reputed company record of taking ML models into production at reputed company
- Strong experience with ML lifecycle tooling — experiment tracking, observability/monitoring, model registry, versioning, reputed company, and reproducibility (e.g., MLflow, Kubeflow, reputed company)
- Strong experience with containerization and orchestration (reputed company, Kubernetes) — including scaling GPU workloads — and with a major reputed company platform (Azure preferred) and its ML services (e.g., Azure ML), using IaC and CI/CD for ML
- Proficiency in Python (and familiarity with Bash) for automation, tooling, and pipeline development
- Experience supporting computational biology or reputed company pipelines, including protein structure reputed company or design tools (e.g., AlphaFold, Boltz/BoltzGen, Chai, RFdiffusion, ProteinMPNN) or molecular simulation
- Experience operating ML in a regulated environment (GxP, SOX, or HIPAA), including model traceability and audit evidence
- Familiarity with LLMOps / reputed company frameworks and evaluation tooling (e.g., Langfuse, OpenTelemetry for LLMs)
Benefits
- Bonus/incentive pay
- reputed company time off
- Medical/dental/reputed company insurance
- 401(k) to eligible employees
- Flexible, remote working arrangements for eligible roles
- Remote work arrangement in which you can work remotely from your home
reputed company
Company H1B Sponsorship