Senior Machine Learning Engineer

Protingent; Washington; Hybrid; USD 180,000 - 240,000; Full time

Job summary

Protingent Staffing seeks a Senior Machine Learning Engineer to own critical ML subsystems and ship production-ready models. You will lead end-to-end ML pipelines, from data prep to inference, and mentor peers while navigating latency, cost, and reliability constraints.

You will work on core systems powering proactive AI products, translating research ideas into scalable solutions and collaborating with research, product, and engineering teams to achieve measurable user impact.

Qualifications

• Shipped ML systems used by real users.
• Experience deploying models to production.
• Strong coding in Python and system design skills.

Responsibilities

• Build core ML systems that power a proactive, long-horizon AI product.
• Own work end-to-end: data preparation, training, evaluation, inference, and iteration.
• Turn research ideas into working systems that run reliably in production.
• Debug model failures and system issues using real production signals.
• Iterate quickly: ship, measure outcomes, refine, and repeat.
• Collaborate closely with research, product, and engineering to deliver real user impact.

Skills

Python

Production code quality

System design

Independent work

Tools

PyTorch

JAX

GPU training

Job description

Senior Machine Learning Engineer

Protingent Staffing has an exciting direct hire Senior Machine Learning Engineer with our client that is fully remote. As a Senior Member of Technical Staff, Machine Learning, you are an independent owner of critical ML subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale. This is a hands‑on, high‑impact role focused on depth.

Job Responsibilities

• Build core ML systems that power a proactive, long‑horizon AI product.
• Own work end‑to‑end: data preparation, training, evaluation, inference, and iteration.
• Turn research ideas into working systems that run reliably in production.
• Debug model failures and system issues using real production signals.
• Iterate quickly: ship, measure outcomes, refine, and repeat.
• Collaborate closely with research, product, and engineering to deliver real user impact.
• Mentor and review work from other ML engineers through example and technical judgment.
• Work under real production constraints: latency, cost, reliability, and safety.
• ML models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets.
• Complex production issues are monitored, debugged, and resolved with minimal disruption.
• Training, inference, and data pipelines are robust, scalable, and maintainable over time.
• Drive measurable improvements in ML systems based on real‑world signals and user feedback.
• Provide mentorship and technical guidance to peers, raising the overall ML engineering standard.
• Collaborate cross‑functionally to ensure ML features integrate seamlessly into products and meet business goals.

Job Qualifications

• Built and shipped ML systems used by real users.
• Understand how modern ML models behave — and misbehave — in production.
• Write strong, production‑quality code and think in systems, not scripts.
• Take ownership, work independently, and push work across the finish line.
• Learn fast, communicate clearly, and improve through iteration.

Must Have

Python, PyTorch / JAX, GPU‑based training and inference systems.

Job Details

• Job Type: Direct Hire
• Pay Range: Market Rate
• Location: Fully Remote