Staff Machine Learning Engineer

Protingent; Washington; Hybrid; USD 180,000 - 250,000; Full time

Job summary

Protingent Staffing is seeking a Staff Machine Learning Engineer to own end-to-end ML system execution, from data pipelines to deployment, for production use.

You will fine-tune models with LoRA/QLoRA, design scalable inference, and build robust evaluation pipelines with research leadership. This remote role demands strong coding and ownership across research, infra, and product teams.

Qualifications

• Experience building real ML systems used in production.
• Ability to handle large models and understand failure modes.
• Strong production-grade coding and system correctness.

Responsibilities

• Own end-to-end ML system execution, including data pipelines and deployment.
• Fine-tune models with LoRA, QLoRA, SFT, DPO, and distillation.
• Architect and operate scalable inference systems balancing latency and cost.
• Design and maintain data systems for synthetic and real training data.
• Implement evaluation pipelines for performance, robustness, safety, and bias.
• Collaborate with application teams to integrate ML into products.
• Ship improvements quickly under production constraints.
• Detect and resolve production issues to minimize user impact.
• Iterate on models to improve user experience over time.
• Support teammates and align on high-impact ML work.

Skills

Python

PyTorch / JAX

GPU-based training & inference

Job description

Staff Machine Learning Engineer

Protingent Staffing presents an exciting direct‑hire opportunity for a Staff Machine Learning Engineer with our client. The role is fully remote.

Job Description

As Technical Lead, Machine Learning, you own the execution layer of our client’s intelligence. You translate research direction into reliable, scalable, production‑grade ML systems. This role sits at the intersection of research, infrastructure, and product, responsible for making models trainable, deployable, observable, and performant under real‑world constraints.

Responsibilities

• Own end‑to‑end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
• Fine‑tune and adapt models using state‑of‑the‑art methods such as LoRA, QLoRA, SFT, DPO, and distillation.
• Architect and operate scalable inference systems, balancing latency, cost, and reliability.
• Design and maintain data systems for high‑quality synthetic and real‑world training data.
• Implement evaluation pipelines covering performance, robustness, safety, and bias in partnership with research leadership.
• Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
• Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.
• Make pragmatic trade‑offs and ship improvements quickly, learning from real usage.
• Work under real production constraints: latency, cost, reliability, and safety.
• Ensure research and models reliably translate into production‑ready solutions with clear performance and quality targets.
• Maintain stable, efficient, and maintainable ML pipelines, training loops, and inference systems.
• Detect, debug, and resolve production issues quickly, minimizing user impact.
• Support team members and align on delivery of high‑impact ML work with minimal friction.
• Iterate on models and systems in measurable, safe ways that improve user experience over time.

Qualifications

You have built or shipped real ML systems used by people, not just demos. You are comfortable working with large models and understanding their failure modes. You write strong, production‑grade code and care about system correctness. You are self‑directed, pragmatic, and take full ownership of outcomes. You communicate clearly and collaborate well in small, high‑trust teams.

Must Have: Python, PyTorch / JAX, GPU‑based training and inference system.

Job Details

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