Bjak - Principal Machine Learning Engineer

• Experience building and shipping ML systems used in production, not just research prototypes. • Strong understanding of modern large-model training, fine-tuning, evaluation, and inference. • Strong software engineering and systems fundamentals. • Experience operating ML workloads at meaningful scale, particularly GPU-based systems. • Strong technical judgment and the ability to navigate ambiguous problems independently. • A bias toward experimentation, measurement, and shipping. • High standards for correctness, reliability, and production quality. • Outcomes • 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. • GPU-based training and inference system • 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. • How We Work • How We Work • We are a small, high-talent-density, hands-on team. Engineers have broad ownership and are expected to exercise strong judgment and execute independently. • We make decisions quickly, work closely together, and balance speed with engineering fundamentals. We care less about process and more about building something exceptional.