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# Tech Lead, Machine Learning

- **Company:** Darwin
- **Location:** Palo Alto, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Training Data, Python (Programming Language), Machine Learning, Software Engineering, Graphics Processing Unit (GPU), Pytorch, Production Code, Machine Learning Operations, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25196_37037806b2723337c8897118d53f7dae

## About the Role

* Experience building and shipping ML systems used in production, not just research prototypes
 * A strong grasp of modern large-model training, fine-tuning, evaluation and inference, and of how large models fail
 * Strong software engineering and systems fundamentals, with production-grade code and high standards for correctness and reliability
 * Experience running ML workloads at meaningful scale, particularly on GPUs
 * Sound technical judgment, and the ability to handle ambiguous problems independently
 * A bias toward experimentation, measurement and shipping

Success looks like

 * Research and models reliably become production-ready solutions with clear performance and quality targets
 * Stable, efficient, maintainable pipelines, training loops and inference systems
 * Production issues found and fixed quickly, with minimal user impact
 * A supported, aligned team delivering high-impact work with little friction
 * Measurable, safe iterations that improve the user experience over time

Tech stack: Python, PyTorch / JAX, GPU-based training and inference systems

## Description

You'll own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems. It's a hands-on leadership role at the intersection of research, systems and product, covering the full model lifecycle: data, training, evaluation, inference and deployment.

What you'll own

 * End-to-end ML systems, from data and training through to evaluation, inference and deployment
 * Training and fine-tuning pipelines for large models
 * Evaluation systems that measure capability, robustness, safety and real-world product performance
 * High-performance inference: latency, GPU utilisation, memory, cost and reliability
 * Data pipelines for high-quality real-world and synthetic training data
 * Production infrastructure to deploy, monitor and continuously improve models
 * Close partnership with research and application engineering, making pragmatic trade-offs and iterating quickly on real-world results