Principal Machine Learning Engineer

About the role

The company builds proactive, agent-first mobile applications that bring intelligence to everyday communication, coordination and collaboration with minimal prompting. As Principal Machine Learning Engineer, you own the execution layer of its intelligence, turning research and model capabilities into reliable, scalable production systems. You work across the full model lifecycle: data, training, evaluation, inference and deployment. This is a hands-on leadership role at the intersection of research, systems and product.

Key responsibilities

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

Required profile

• Experience building and shipping ML systems used in production, not just research prototypes.
• Strong software engineering and systems fundamentals.
• Experience operating ML workloads at meaningful scale, especially GPU-based systems.
• Strong technical judgment and the ability to solve ambiguous problems independently.
• Bias toward experimentation, measurement and shipping, with high standards for correctness and production quality.

Required skills

• Python
• PyTorch / JAX
• Large-model training, fine-tuning, evaluation and inference
• GPU-based training and inference systems

Applications are evaluated by the technical team and interviews are held virtually and/or onsite; the interview process involves up to four interviews. The team is described as small and hands-on, with engineers given broad ownership.