Lead Machine Learning Engineer (AI Systems)

Quick Facts

• Role: Lead Machine Learning Engineer (AI Systems)

• Work model: 100% Remote (Europe)

• Interview process: 3–4 interviews

Description

Lead end-to-end development of production-ready machine learning systems for proactive, agent-first AI applications. Own the full ML lifecycle—from training and evaluation to inference optimization and deployment—while collaborating with research and engineering to bring model capabilities into the product.

Responsibilities

• Build and own end-to-end ML systems running in production

• Develop training and fine-tuning pipelines for large models

• Design evaluation frameworks to measure model quality, robustness, and real-world performance

• Optimize GPU-based inference for latency, memory, cost, and scalability

• Build reliable data pipelines for real-world and synthetic training data

• Develop production infrastructure for model deployment, monitoring, and continuous improvement

• Collaborate with research and application engineering teams to integrate model capabilities into the product

• Drive technical decisions and support other engineers while remaining hands-on

Requirements

• Strong Python skills and hands-on experience with PyTorch and/or JAX

• Proven experience building and shipping production ML systems

• Solid understanding of large-model training, fine-tuning, evaluation, and inference

• Experience working with GPU-based ML workloads at meaningful scale

• Strong software engineering fundamentals, system design, and performance optimization skills

• Ability to independently solve complex problems and take ownership of technical decisions

• Pragmatic, hands-on mindset focused on experimentation, reliability, and shipping

• Technical leadership experience is a plus

Benefits

• End-to-end ownership across the ML lifecycle (training, evaluation, inference, deployment)

• Opportunity to influence architecture and technical decisions early in the product lifecycle

• Challenging engineering with large models and GPU infrastructure in performance-critical ML systems

• Real product impact: your work directly shapes how people interact with AI in everyday life

• Small, highly technical team with autonomy, fast decisions, and minimal bureaucracy

• 100% remote work from anywhere in Europe