Principal Machine Learning Engineer
# Principal Machine Learning Engineer **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]` `[Principal-Machine-Learning-Engineer]` `[AI-Engineer]` `[Deep-Learning-Engineer]` `[Principal-ML-Engineer]` `[Principal-AI-ML-Engineer]` `[Principal-Machine-Learning-Scientist]` `[Principal-Machine-Learning-Architect]` `[Principal-AI-Engineer]` `[Principal-ML-Platform-Engineer]` `[Lead-Machine-Learning-Engineer]` `[Senior-Staff-Machine-Learning-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 You'll be responsible for turning research direction into working, production-grade ML systems. This role owns the execution layer of the company's intelligence — training pipelines, inference systems, evaluation tooling, and deployment. What You'll Work On - Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and 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 - Strong background in deep learning and transformer-based architectures - Hands-on experience training, fine-tuning, or deploying large-scale ML models in production - Proficiency with at least one modern ML framework (PyTorch, JAX), and ability to pick up others quickly - Experience with distributed training and inference frameworks (DeepSpeed, FSDP, Megatron, ZeRO, Ray) - Strong software engineering fundamentals — you write robust, maintainable, production-grade systems - Experience with GPU optimization, including memory efficiency, quantization, and mixed precision - Comfort owning ambiguous, zero-to-one ML systems end-to-end - A bias toward shipping, learning fast, and improving systems through iteration Nice to Have - Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer - Contributions to open-source ML or systems libraries - Background in scientific computing, compilers, or GPU kernels - Experience with RLHF pipelines (PPO, DPO, ORPO) - Experience training or deploying multimodal or diffusion models - Experience with large-scale data processing (Apache Arrow, Spark, Ray) 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
- Principal-Machine-Learning-Engineer
- AI-Engineer
- Deep-Learning-Engineer
- Principal-ML-Engineer
- Principal-AI-ML-Engineer
- Principal-Machine-Learning-Scientist
- Principal-Machine-Learning-Architect
- Principal-AI-Engineer
- Principal-ML-Platform-Engineer
- Lead-Machine-Learning-Engineer
- Senior-Staff-Machine-Learning-Engineer
Наблюдалась 2026-10-16, впервые 2026-10-16, источник — Himalayas (JSON API).