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Mid-Senior Full Stack Engineer (Machine Learning Company)

Oxen.AI

Remote · Los Angeles, CA · Posted October 27, 2026

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  • AWS
  • Machine learning
  • CI/CD
  • Kubernetes
  • GCP

About the job

What You'll Do:- Design, build and maintain backend services in Elixir (Phoenix/OTP) and/or Rust for Oxen.ai's data and model platform.- Ship full features end to end: APIs, data models, background jobs and the product UI that sits on top of them.- Build reliable, concurrent systems for model inference, large-scale dataset workflows and fine-tuning pipelines.- Integrate the 200+ models we serve and the cloud storage systems behind our repositories.- Share ownership of customer support with the rest of the engineering team: answer questions, debug issues alongside customers, and turn what you learn into product fixes.- Work closely with product, ML and design to turn customer feedback into shipped improvements quickly.- Improve performance, observability and reliability across the stack.- Contribute to open-source projects and community engagement around Oxen.ai. What You Have (Skills & Experience):- 4+ years of professional software engineering experience, including significant production work in Elixir (or Rust, with real interest in Elixir).- A strong grasp of OTP, concurrency and fault tolerance, or comparable depth in Rust's async and ownership model.- Experience building and shipping web applications across the stack, from database schema to user interface.- Solid API design instincts and comfort with relational databases (Postgres or similar).- A generalist mindset: you're happy to pick up a new language, tool or part of the codebase when the problem calls for it.- Comfort working directly with customers: you communicate clearly, stay patient when someone is blocked, and can explain technical problems and fixes to people of any background.- Strong communication skills and a collaborative mindset suited to early-stage teams. Nice to have:- Next.js / React experience building polished, production front ends.- Infrastructure experience, such as cloud (AWS/GCP), Kubernetes, Terraform, CI/CD, or running GPU or inference workloads.- Hands-on experience in machine learning or AI environments.- Open-source contributions.