ML Performance Engineer

ppWe're looking for a performance-focusedbML Engineer /bto help speed up large-scale model training by optimizing our internal stack and compute infrastructure. You'll work across the full training pipeline - from GPU kernels to system-level throughput - applying profiling, CUDA-level tuning, and distributed systems techniques. The goal is to reduce training time, boost iteration speed, and use compute more efficiently. /p pThis is a key role in a growing team building deep technical expertise in ML training systems. /p h3Responsibilities /h3 ul liOptimize our model training pipeline to improve both speed and reliability, enabling faster and more efficient experimentation; /li liApply GPU-level optimization techniques using tools like JAX, Triton, low-level CUDA to improve training performance and efficiency at scale; /li liIdentify and resolve performance bottlenecks across the entire ML pipeline - from data loading and preprocessing to CUDA kernels; /li liBuild tools and extend internal infrastructure to support scalable, reproducible, and high-performance training workflows; /li liMentor and support engineers and researchers in adopting performance best practices across the team; /li liHelp grow the team’s GPU and systems-level capabilities, and contribute to a culture of engineering excellence and rapid experimentation. /li /ul h3Requirements /h3 ul liDemonstrated experience optimizing neural network training in production or large-scale research settings - e.g. reducing training time, improving hardware utilization, or accelerating feedback cycles for ML researchers; /li liExtensive practical experience with ML frameworks such as PyTorch or JAX; /li liHands-on experience with training and optimizing deep learning architectures such as LSTM and Transformer-based models, including different attention mechanisms; /li liExperience working with CUDA, Triton, or other low-level GPU technologies for performance tuning; /li liProficiency in profiling and debugging training pipelines, using tools such as Nsight/cprofiler/CUDA/gdb/torch profiler; /li liUnderstanding of distributed training concepts (e.g. data/model/tensor/sequence/pipeline/context parallelism, memory and compute tradeoffs); /li liA collaborative and proactive mindset, with strong communication skills and the ability to mentor teammates and partner effectively within the team; /li liStrong proficiency in Python for building infrastructure-level tooling, debugging training systems, and integrating with ML frameworks and profiling tools; /li /ul h3What we offer /h3 ul liHigh base salary and social benefits; /li liGenerous bonus structure. We are very flexible in discussing salary and conditions of employment; /li liCutting-edge hardware and software in production as well as high technical expertise of the company which allows implementation of bold ideas and boosting great results. Ownership over initiatives that directly solve business problems; /li liAbility to trade on dozens of international exchanges; /li liFlexible workflow (lack of formalism and bureaucracy, no pressure and over-management) and working schedule; /li liTuition reimbursement, conference and training sponsorship; /li liLocation: Amsterdam preferred. /li /ul /p #J-18808-Ljbffr