Technical Lead, Machine Learning

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

Job summary

Protingent Staffing is seeking a Technical Lead, Machine Learning for a direct-hire opportunity with a client offering fully remote work. You will own the execution layer of AI systems, translating research into production-ready pipelines and scalable inference.

You will lead end-to-end ML system development, optimize models for latency and cost, and collaborate with engineering teams to ship robust, observable solutions.

Qualifications

• Shipped real ML systems used by people.
• Familiar with large models and failure modes.
• Writes production-ready, correct code and takes ownership.

Responsibilities

• Own end-to-end ML system execution: data pipelines, training workflows, evaluation, inference, and deployment.
• Fine-tune models using state-of-the-art methods (LoRA, QLoRA, SFT, DPO, distillation).
• Architect and operate scalable inference systems balancing latency and cost.
• Design data systems for high-quality synthetic and real training data.
• Implement evaluation pipelines for performance, robustness, safety, and bias with research leadership.

Skills

Python

PyTorch/JAX

GPU training

Job description

Technical Lead, Machine Learning

Protingent Staffing has an exciting direct hire Technical Lead, Machine Learning with our client that is fully remote.

As Technical Lead, Machine Learning, you own the execution layer of A1’s intelligence. You translate research direction into reliable, scalable, production‑grade ML systems. This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real‑world constraints.

Job 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.
• Research and models reliably translate into production‑ready solutions with clear performance and quality targets.
• Ensure ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.
• Detect, debug, and resolve production issues quickly, minimizing user impact.
• Support team members so they can deliver high‑impact ML work with minimal friction.
• Make iterations on models and systems measurable, safe and improving user experience over time.

Job 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 HirePay Range: Market RateLocation: Fully Remote.