Technical Lead, Machine Learning
# Technical Lead, Machine Learning **DNA325** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Machine-Learning-Engineer]` `[AI-Engineer]` `[Technical-Lead]` `[Applied-Scientist]` `[Machine-Learning-Technical-Lead]` `[Machine-Learning-Lead]` `[Lead-Machine-Learning-Engineer]` `[Machine-Learning-Engineering-Lead]` `[AI-Technical-Lead]` `[Senior-AI-Technical-Lead]` `[Lead-ML-Engineer]` `[Lead-Machine-Learning-Scientist]` `[Technical-Lead-AI]` `[Lead-AI-ML-Engineer]` --- ### About the Role 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. Originally posted on Himalayas
- Machine-Learning-Engineer
- AI-Engineer
- Technical-Lead
- Applied-Scientist
- Machine-Learning-Technical-Lead
- Machine-Learning-Lead
- Lead-Machine-Learning-Engineer
- Machine-Learning-Engineering-Lead
- AI-Technical-Lead
- Senior-AI-Technical-Lead
- Lead-ML-Engineer
- Lead-Machine-Learning-Scientist
- Technical-Lead-AI
- Lead-AI-ML-Engineer
Наблюдалась 2026-10-16, впервые 2026-10-16, источник — Himalayas (JSON API).