Technical Lead, Machine Learning

European Recruitment BV; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

European Recruitment BV is seeking a Technical Lead, Machine Learning to own the execution layer of ML systems. You will translate research directions into reliable, scalable, production-grade ML pipelines, training workflows, and inference architectures.

You will balance latency, cost, and reliability while fine-tuning models with LoRA, QLoRA, SFT, DPO, and distillation, and collaborate with research leadership and application teams to deliver production-ready solutions.

Qualifications

• Built or shipped real ML systems used by people.
• Comfortable with large models and understanding failure modes.
• Write production-grade code with emphasis on system correctness.
• Self-directed, pragmatic, and takes ownership of outcomes.
• Clear communication and collaboration in small, high-trust teams.

Responsibilities

• Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
• Fine-tune and adapt models using 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 with research leadership.
• Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
• Collaborate with application engineering to integrate ML systems 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.

Skills

Production-grade coding

System design

Ownership & accountability

Cross-functional collaboration

Pragmatic decision making

Tools

PyTorch

JAX

Job description

Role

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.

What You'll Do

• 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.

Outcomes

• Research and models reliably translate into production-ready solutions with clear performance and quality targets.
• ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.
• Production issues are detected, debugged, and resolved quickly, minimizing user impact.
• Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction.
• Iterations on models and systems are measurable, safe, and improve user experience over time.

Tech Stack

• Python
• PyTorch / JAX
• GPU-based training and inference system

Ideal Experience

• 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.