Technical Lead, Machine Learning
Summary
Technical Lead, ML at an AI-native product company (a DNA325 client) rebuilding email, notes, and task tools. Owns end-to-end production ML: data pipelines, fine-tuning (LoRA, QLoRA, SFT, DPO, distillation), evaluation, and GPU-based inference deployment. Core stack: Python, PyTorch/JAX, GPU training and inference systems.
Our client is an AI-native product company built to replace how billions of people manage their digital lives — starting with email, notes, and task tools that were never designed to be AI-native. It's backed by multi-million-dollar investment and built remote-first from day one, with a clear target: cut the time it takes users to get everyday things done by roughly 90%. That means solving the problems most AI products avoid — long-running workflows, persistent context, and reliable behavior under real-world, non-deterministic conditions. The team is small and high-talent-density by design, not by necessity, and moves at a pace that matches the scale of what it's building.
About the Role
As Technical Lead, Machine Learning, you own the execution layer of the company's intelligence — translating research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product: you're responsible for making models trainable, deployable, observable, and performant under real-world constraints.
What You'll Do
Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, deployment
Fine-tune and adapt models using state-of-the-art methods — LoRA, QLoRA, SFT, DPO, distillation
Architect and operate scalable inference systems, balancing latency, cost, and reliability
Design and maintain data systems for high-quality synthetic and real-world training data
Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership
Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies
Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products
Make pragmatic trade-offs and ship improvements quickly, learning from real usage
Work under real production constraints: latency, cost, reliability, and safety
Requirements
You've built or shipped real ML systems used by people, not just demos
You're comfortable working with large models and understanding their failure modes
You write strong, production-grade code and care about system correctness
You're self-directed, pragmatic, and take full ownership of outcomes
You communicate clearly and collaborate well in small, high-trust teams
Tech Stack
Python · PyTorch / JAX · GPU-based training and inference systems
What Success Looks Like
Research and models reliably translate into production-ready solutions with clear performance and quality targets
ML pipelines, training loops, and inference systems are stable, efficient, and maintainable
Production issues are detected, debugged, and resolved quickly, minimizing user impact
Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction
Iterations on models and systems are measurable, safe, and improve user experience over time
What to Expect
The best products in the world are built by small, world-class teams. Decisions are made collectively, at rapid speed — balancing high-quality shipping with fast learning. You'll be expected to bring structure, exercise judgment, and execute independently.
Compensation and benefits are competitive and vary by location; the package includes base salary and equity, discussed openly with you as part of the process.
If there's a fit, expect 3–4 interviews total, followed by a prompt, transparent decision.