AI Engineer

📋 Description

• Research and develop machine learning models for perception and understanding problems across
• Explore how specialized models and larger general-purpose models can work together in production
• Design data, training, and evaluation approaches that hold up under real-world conditions, not just
• Study model behavior, robustness, and failure modes across data, deployment, and operating
• Integrate and validate new capabilities in real-time or resource-constrained systems.
• Work with firmware, platform, and product teams to turn research into working systems.

🎯 Requirements

• Understand deep-learning fundamentals: architectures, training dynamics, evaluation design, and
• Think in systems: latency and memory budgets, failure modes, distributed pipelines, and on-device
• Take end-to-end ownership across the model, edge, and cloud boundaries, and stay comfortable with
• Thrive in fast-paced environments and can rapidly iterate from experimentation to production.
• Have hands-on experience using agentic development tools and AI-assisted coding as a core part of
• Are proficient in Python and PyTorch, with working ability in at least one systems language (e.g.