Machine Learning Engineer

This contract remote role focuses on building and operating the compute platform, MLOps, and agentic tooling that supports protein therapeutic design. The work connects Kubernetes-based orchestration and monorepo workflows to data and model infrastructure feeding downstream analytics in Snowflake.

Key Responsibilities

• Develop and maintain production Python services, pipelines, and CLI tooling.
• Build and maintain Helm charts, container images, and deployment infrastructure as part of the daily workflow.
• Deploy and operate workloads on Kubernetes across multiple AWS regions and accounts.
• Instrument pipelines and models so performance, cost, and benchmark results are measurable.
• Partner with scientists and engineering leads to determine what to build, and support adoption through documentation and hands-on guidance.

Required Qualifications

• Bachelor’s degree with 7–10 years of relevant experience, or a Master’s degree with 5 or more years of relevant experience.
• Strong Python development skills, including experience building production-grade tooling or pipelines.
• Working knowledge of Kubernetes, Helm, and container-based deployments.
• Experience developing, deploying, or optimizing machine learning pipelines in a production environment.
• Familiarity with infrastructure-as-code and CI/CD practices.
• Experience with AWS, including multi-region or multi-account deployments.
• Experience with data warehousing and ELT patterns, with Snowflake preferred.
• Experience operating on a small team (approximately 3–10 engineers) where engineers own systems end to end, set priorities within a goal, and ship without a dedicated QA, SRE, or release function.
• Routine use of agentic coding harnesses such as Claude Code, OpenCode, Codex, or equivalent, including understanding where they help and where they do not.
• Willingness to learn computational biology and protein science domains.
• Effective communication skills for independent remote work and collaboration with both engineers and scientists.

Relevant Technologies

• Python
• Kubernetes
• Helm
• Containerized deployments
• AWS
• S3
• Snowflake
• CI/CD
• Infrastructure-as-code
• Claude Code, OpenCode, Codex

Scope of Work

• Extend the Kubernetes orchestration platform and the workflows that run on it, including migrating existing application backends onto it.
• Make the platform region-agnostic so compute can be scheduled in new AWS regions and accounts using configuration rather than code changes.
• Build the registration, versioning, and serving path for models so outputs are available to scientific applications and downstream reporting.
• Integrate model output with the data layer instead of leaving results as per-project artifacts.
• Build automated benchmarking and retraining for computational tools, LLM models, and agent skills used in de novo design, including GPU-backed benchmarking of larger models, test-set and dataset tracking, and persistent benchmark history.
• Incorporate agentic AI into existing scientific applications by defining tools and skills, creating evaluation harnesses, and implementing safe plumbing for agent calls to internal services.
• Build reliable S3-to-Snowflake data pulls and automated registration of de novo design results, with monitoring and backfill behavior that does not require manual intervention.
• Reduce runtime and cost of existing production pipelines through profiling, GPU utilization, batching, scheduling, and container image improvements.

Contract Details

• Location: Remote
• Job type: Contract
• Pay: USD 48–58 per hour
• Minimum experience: 5 years
• Education: Master’s degree

Benefits

• Automatic life insurance, plus short-term and long-term disability insurance.
• Optional health, dental, and vision insurance for full-time employees.
• College loan assistance.
• Employer matched retirement savings plan.

Company Overview

Spectrix Analytical Services, LLC is a dedicated provider of on-site analytical chemistry services, delivering reliable and high-quality results since 1999. The team of experienced scientists supports research and development efforts across various industries by generating precise analytical data and maintaining sophisticated instrumentation directly at client laboratories.