Senior Machine Learning Software Engineer, Research

Physicsx · Londonsenior

In short

▸Ingeniero de software ML senior en investigación, impulsando simulaciones de física avanzadas con IA.
▸Diseño y escalado de modelos ML distribuidos, trabajo con científicos de investigación y mentoreo de equipos.
▸Destacado por su impacto en la estrategia técnica y cultura de investigación en un entorno de alta innovación.

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What they ask for

✓Experiencia comprobada en ingeniería de software ML a nivel senior.
✓Capacidad para liderar estrategias técnicas en investigación y desarrollo.
✓Experiencia en arquitecturas de entrenamiento distribuido (data parallelism, parameter server, etc.).
✓Habilidad para transformar prototipos en implementaciones robustas y escalables.
✓Capacidad de mentoreo y desarrollo de colegas más junior.
✓Conocimiento profundo en deep learning y/o métodos probabilísticos para problemas de ingeniería y física.

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PythonPyTorchTensorFlowCUDAAWSAzureGCPDockerKubernetesGit

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 • Shape Research group strategy and culture in a significant way, especially in domains of expertise. • Be opinionated and formulate strategy on engineering topics relevant to our Research priorities, especially on: scaled engineering, securing compute, infrastructure stack. • Define necessary profiles to execute this strategy. • Promote effective working patterns and proactively flag issues with team dynamics to foster a productive environment. • Nurture younger colleagues to grow their skillset and guide their professional development. • Own Research work-streams at a high-level to deliver outcomes. • Align priorities with problem stakeholders, internal and external. • Set the technical direction for the stream and apply judgement and taste to drive progress. • Plan roadmaps with clear milestones for key decisions and outcomes. • Organise and guide the more junior members of the team to effectively execute and deliver against this roadmap. • Communicate purpose and key outcomes to raise awareness across the company and create opportunities for use and deployment. • The below activities in particular. • 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. • 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.

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