• PyTorch / JAX
• GPU‑based training and inference systems
• IDEAL BACKGROUND
• Experience building and shipping ML systems used by real users.
• Strong understanding of how modern ML models behave — and misbehave — in production.
• Ability to write production‑quality code and think in systems, not scripts.
• Independent ownership: driving work across the finish line.
• Fast learner, clear communicator, iterative mindset.
• EXPECTED OUTCOMES
• ML models and systems consistently meet accuracy, latency, reliability, and efficiency targets.
• Complex production issues are monitored, debugged, and resolved with minimal disruption.
• Training, inference, and data pipelines are robust, scalable, and maintainable.
• Measurable improvements in ML systems based on real‑world signals and user feedback.
• Technical guidance and mentorship that raises the overall ML engineering standard.
• Seamless integration of ML features into products that meet business goals.
• Our client A1 is a small, world‑class team with high talent density. They move quickly, make decisions collectively, and balance shipping high‑quality work with rapid learning. Structure, sound judgment, and the ability to execute independently are highly valued.