Lead Engineer, Machine Learning

BJAK is hiring a Lead Engineer, Machine Learning to turn research and model capabilities into reliable, scalable production systems. The role owns the machine-learning lifecycle, from data and training through evaluation, inference, and deployment, while partnering with research and application engineering to improve the product.

Responsibilities:

• Own the end-to-end ML systems powering our company, from data and training to evaluation, inference, and deployment
• Build and evolve training and fine-tuning pipelines for large models
• Design evaluation systems that measure capability, robustness, safety, and real-world product performance
• Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability
• Build data pipelines and systems for high-quality real-world and synthetic training data
• Establish reliable production infrastructure for deploying, monitoring, and continuously improving models
• Partner closely with research and application engineering to turn model capabilities into product improvements
• Make pragmatic technical trade-offs and rapidly iterate based on real-world performance

Requirements:

• 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
• Python
• PyTorch / JAX
• 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