Machine Learning Software Engineer, Research

Physicsx · Londonmid

In short

▸Ingeniero de software de ML enfocado en simulaciones físicas de alto rendimiento.
▸Trabajas en modelos escalables, entrenamiento distribuido y deployments en cloud para ingeniería avanzada.
▸Destaca la fusión de investigación científica y ingeniería de software en entornos de hardware innovador.

Comunicación efectiva en inglés con equipos y clientes.

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What they ask for

✓MSc o PhD en ciencias, ingeniería, matemática o campos afines.
✓Experiencia >2 años en roles data-driven con ML y modelado.
✓Habilidad para escalar modelos de Aprendizaje Profundo en entornos distribuidos.
✓Conocimiento en computación de alto rendimiento (GPU, CPU, clusters).
✓Familiaridad con frameworks distribuidos: Spark, Dask, MPI, OpenMP, CUDA, Triton.
✓Experiencia con cloud (AWS, Azure, GCP) y entrenamiento en múltiples GPUs.

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PythonPyTorchTensorFlowCUDATritonMPIOpenMPSparkDaskAWS

Who should you write to at Physicsx?

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About us PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive. Note: We are currently recruiting for multiple positions across different levels, however please only apply for the role that best aligns with your skillset and career goals. What you will do • Work closely with our research scientists and simulation engineers to build and deliver models that address real-world physics and engineering problems. • Design, build and optimise machine learning models with a focus on scalability and efficiency in our application domain. • Transform prototype model implementations to robust and optimised implementations. • Implement distributed training architectures (e.g., data parallelism, parameter server, etc.) for multi-node/multi-GPU training and explore federated learning capacity using cloud (e.g., AWS, Azure, GCP) and on-premise services. • Work with research scientists to design, build and scale foundation models for science and engineering; helping to scale and optimise model training to large data and multi-GPU cloud compute. • Identify the best libraries, frameworks and tools for our modelling efforts to set us up for success. • Own Research work-streams at different levels, depending on seniority. • Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems. • Work at the intersection of data science and software engineering to translate the results of our Research into re-usable libraries, tooling and products. • Foster a nurturing environment for colleagues with less experience in ML / Engineering for them to grow and you to mentor. What you bring to the table • Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering. • Ability to work autonomously and scope and effectively deliver projects across a variety of domains. • Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. • Excellent collaboration and communication skills — with teams and customers alike. • MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following: • Scientific computing; • High-performance computing (CPU / GPU clusters); • Parallelised / distributed training for large / foundation models. • Ideally >2 years of experience in a data-driven role in a professional setting , with exposure to: • scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus); • distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton); • cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP); • building machine learning models and pipelines in Python, using common libraries and frameworks (

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