Job Duties:

• Build and improve ML components across data, training, evaluation, and inference.
• Fine-tune and adapt models as part of larger production systems.
• Implement evaluation and testing to understand model behavior.
• Help build and maintain data pipelines for real-world and synthetic data.
• Debug model issues, performance problems, and production incidents.
• Ship improvements iteratively and learn from real user feedback.
• Work closely with senior ML engineers and product teams.
• Work under real production constraints: latency, cost, reliability, and safety
• Ensure ML models in production meet expected accuracy, latency, and reliability targets.
• Production issues are identified quickly, debugged effectively, and root causes addressed.
• Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable.
• Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features.
• Iterations on models and systems are driven by real-world signals and measurable improvements.

Technical Experience/ Requirements:

• Python, PyTorch / JAX, Production ML systems running on GPUs
• Strong foundations in machine learning and modern neural architectures.
• Some hands-on experience training, fine-tuning, or deploying ML models.
• Comfortable writing production-quality code and learning new tools quickly.
• Curious, coachable, and eager to learn from real systems in production.
• Able to work through ambiguity with guidance and grow ownership over time.
• Bias toward shipping, iteration, and continuous improvement.