IMC · Amsterdam, Netherlands; London, United Kingdom
Machine Learning Engineer
最近核查時仍有刊登最近確認刊登：2026-11-05 18:37:34.566853+00
招聘狀態可能變動，申請前請查看原始職位。
薪酬未公開
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經驗3+ years' experience in machine learning, with a focus on training or inference systems.
來源發布日期：2025-04-01T15:20:28-04:00
職位要求
- As a team, our Machine Learning Engineers broadly work across two areas: ML Research Engineering, focused on the systems and frameworks researchers use to train, evaluate and iterate on models at scale; and ML Performance Engineering, focused on making ML workloads execute as efficiently as possible, typically through GPU and low-level systems optimisation. These aren't rigid tracks, and there is overlap between them. You will bring depth of expertise in at least one of these focus areas and collaborate closely with global team members.
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原始資料於 2026-11-04T13:50:39.824945+00:00 查閱；內容可能已更新，請查看原始職位。
Your Skills and Experience: Core, regardless of focus area: 3+ years' experience in machine learning, with a focus on training or inference systems. Production software engineering: Python, testing discipline and genuine ownership of systems once they ship. Machine learning fundamentals: you understand modern ML workloads beyond high-level APIs and can reason about model behaviour, correctness and performance at the level relevant to your area of expertise. A systems mindset: you reach for measurement before opinion on performance, scale or reliability issues, and can make sound build-versus-buy decisions. Strong collaboration: comfortable explaining the reasoning behind your technical decisions, working through differing perspectives with researchers and adapting your approach when the evidence supports it. Depth in one or more of the following: Distributed training and large-scale systems: parallelism strategy, data pipelines and checkpointing; frameworks such as PyTorch, TensorFlow or JAX. GPU performance and low-level optimisation: reasoning about memory, compute and latency at the device level, and optimising performance across kernels, compilers and hardware (e.g. CUDA, Triton or equivalent). Production inference systems: hands-on experience building and hardening real-time, low-latency prediction pipelines in high-performance environments. Numerics and correctness at scale: low-precision effects, KV caching, batching, and knowing which optimisations stay bit-exact. Also a plus: Exposure to cloud platforms and orchestration tools. A track record of contributing to open-source projects in machine learning, data science or distributed systems.
來源：公司官网 · 資料查閱日期 2026-11-04T13:50:39.824945+00:00。職位狀態及要求請以原始招聘頁面為準。