Machine Learning Engineer

Contract remote role for a Machine Learning Platform and Agentic AI Engineer building compute, MLOps, and tooling that supports protein therapeutic design.

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 AWS accounts.
• Instrument pipelines and models so performance
  , cost
  , and benchmark results are measurable.
• Collaborate with scientists and engineering leads to decide what to build.
• Support adoption through documentation and hands‑on help.

Requirements

• Bachelor’s degree with 7–10 years of relevant experience, or Master’s degree with 5+ 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 production.
• Familiarity with infrastructure-as-code and CI/CD practices.
• AWS experience, including multi‑region or multi‑account deployments.
• Data warehousing and ELT pattern experience, with Snowflake preferred.
• Experience working on a small team (approximately 3–10 engineers) with end‑to‑end ownership, self‑directed prioritization within goals, and shipping without dedicated QA
  , SRE
  , or release functions.
• Routine use of agentic coding harnesses such as Claude Code
  , Open Code
  , Codex
  , or equivalent, including judgment on where they help and where they do not.
• Willingness to learn computational biology and protein science domains.
• Effective communicator able to work independently in a remote environment and collaborate with engineers and scientists.

Key Work Areas

• Extend the Kubernetes orchestration platform and the workflows running 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 via configuration rather than code changes.
• Build model registration
  , versioning
  , and the serving path so model outputs are available to scientific applications and downstream reporting.
• Integrate model output with the data layer rather than keeping it as per‑project artifacts.
• Build automated benchmarking and retraining for computational tools,
  LLM models
  , and agent skills used in de novo design.
• Include GPU‑backed benchmarking of larger models, test‑set and dataset tracking, and persistent benchmark result history over time.
• Incorporate agentic AI into existing scientific applications by defining tools and skills, creating evaluation harnesses, and adding the plumbing that lets agents call internal services safely.
• Build S3‑to‑Snowflake data pulls and automated registration of de novo design results, including monitoring and backfill behavior without manual intervention.
• Reduce runtime and cost of existing production pipelines through profiling, GPU utilization, batching, scheduling, and container image work.

Technologies

• Python
• Kubernetes
• Helm
• Containerized deployments
• AWS
• S3
• Snowflake
• CI/CD
• Infrastructure‑as‑code
• Claude Code
• Open Code
• Codex

Compensation & Employment

• Location: Remote
• Job type: Contract
• Pay: USD 48–58 per hour

Benefits

• Automatic benefits of life insurance and short 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 provides on‑site analytical chemistry services with reliable, high‑quality results since 1999.
• Team of experienced scientists supports R&D across various industries by generating precise analytical data and maintaining sophisticated instrumentation directly at clients’ laboratories.