Senior Applied Research Engineer | Barcelona, Spain, Hybrid

Senior Applied Research Engineer | Barcelona, Spain

We are partnered with a cutting-edge AI company shaping the future of enterprise decision-making. Founded by experienced technologists from leading research environments, the firm has developed a market-leading platform purpose-built for the structured data that underpins critical business decisions. Backed by top-tier investors and trusted by some of the world’s largest organisations, the company helps enterprises unlock significant value by enabling more accurate, forward-looking decision-making.

You will work on novel technical challenges in large-scale model development and contribute to technology that is changing how major organisations operate. This is an opportunity to join a category-defining company at an early stage and help shape its trajectory.

📍Location: Barcelona, Spain, Hybrid working - Company offers relocation support if needed.

💶 Salary: Highly competitive Base (€) + Equity and other benefits

⌛ Permanent Role

Key responsibilities

• Profile end-to-end distributed training runs to identify bottlenecks across compute, GPU memory, and inter-GPU communication.
• Influence architectural decisions to improve efficiency and reliability of large-scale training jobs, including developing Triton/CUDA kernels when needed.
• Design and implement model scaling, parallelisation, and memory optimisation techniques for training workloads with very large context sizes.
• Collaborate closely with ML Researchers to diagnose architectural inefficiencies, ensure new research ideas scale efficiently in practice, and share internal knowledge on optimisation.
• Drive productionisation and serving of models from the research side, including improving inference efficiency via techniques such as quantisation.

Must have

• Strong understanding of modern ML architectures and large-scale training pipelines.
• Hands-on experience running distributed training jobs on multi-GPU systems.
• Advanced profiling and debugging across CPU, GPU, memory usage, latency, and inter-GPU communication.
• Strong programming skills in Python.
• Experience with model scaling and parallelisation strategies, including tensor and pipeline parallelism.

Nice to have

• Familiarity with NCCL, MPI, and distributed communication primitives.
• Knowledge of PyTorch and Triton internals.
• Programming experience with C++ and CUDA.

Benefits

• Competitive compensation with salary and equity and comprehensive benefits
• Relocation support for employees moving to join the team in the office location.
• A mission-driven, low-ego culture valuing diversity of thought, ownership, and bias towards action.