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# Machine Learning Engineer

- **Company:** Spectrix Analytical Services, LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $99,840.0 - $120,640.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Computational Biology, Continuous Integration, Data Warehousing, Python (Programming Language), Machine Learning, Large Language Models, Claude Code, Snowflake, Kubernetes, Machine Learning Operations
- **Published:** October 8, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a7e1a222e582b009

## About the Role

* 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 with 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, Snowflake preferred.
 * Experience on a small team (roughly 3-10 engineers) where individuals own systems end to end, set their own priorities within a goal, and ship without a dedicated QA, SRE, or release function.
 * Routine use of agentic coding harnesses - Claude Code, OpenCode, Codex, or equivalent - as part of day-to-day development, with a view on where they help and where they do not.
 * Exposure to Rust is a plus.
 * Experience building LLM agents, tool definitions, or evaluation harnesses is a plus.
 * Willingness to learn computational biology and protein science domains.
 * Effective communicator who can work independently in a remote environment and collaborate with both engineers and scientists.

## Description

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