Machine Learning Software Engineer, Research

PhysicsX is a deep-tech company of scientists and engineers, developing machine learning applications to massively accelerate physics simulations and enable a new frontier of optimisation opportunities in design and engineering.  

Born out of numerical physics and proven in Formula One, we help our customers radically improve their concepts and designs, transform their engineering processes and drive operational product performance. We do this in some of the most advanced and important industries of our time – including Space, Aerospace, Additive Manufacturing, Electric Vehicles, Motorsport, and Renewables. Our work creates positive impact for society, be it by improving the design of artificial hearts, reducing CO2 emissions from aircraft and road vehicles, and increasing the performance of wind turbines.   

We are taking the next leap in building out our technology platform and product offering. In this context, we are looking for a capable and enthusiastic machine learning software engineer to join our research team.

This is a hybrid role, meaning you'll enjoy the best of both worlds- flexibility and in-person collaboration. We work together in our NYC office to foster innovation, teamwork and problem-solving. If you're in (or ready to move to) New York and want to be part of a dynamic, hands-on environment, we'd love to hear from you!

Note: We do not provide visa sponsorship in the US. Please only apply if you have the right to work in the US.

• 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 optimize machine learning models with a focus on scalability and efficiency in our application domain.
• Transform prototype model implementations to robust and optimized 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 optimize 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.

• 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.

• Scientific computing;
• High-performance computing (CPU / GPU clusters);
• Parallelised / distributed training for large / foundation models.

• scaling and optimizing 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 (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications;
• C/C++ for computer vision, geometry processing, or scientific computing;
• software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);
• container-ization and orchestration (Docker, Kubernetes, Slurm);
• writing pipelines and experiment environments, including running experiments in pipelines in a systematic way.

• Be part of something larger: Make an impact and meaningfully shape an early-stage company. Work on some of the most exciting and important topics there are. Do something you can be proud of 
• Work with a fun group of colleagues that support you, challenge you and help you grow. We come from many different backgrounds, but what we have in common is the desire to operate at the very top of our fields and solve truly challenging problems in science and engineering. If you are similarly capable, caring and driven, you'll find yourself at home here 
• Experience a truly flat hierarchy. Voicing your ideas is not only welcome but encouraged, especially when they challenge the status quo 
• Work sustainably, striking the right balance between work and personal life.
• Receive a competitive compensation and equity package, in addition to plenty of perks such as generous vacation and parental leave, complimentary office food, as well as fun outings and events   
• Opportunity to collaborate in our lovely Shoreditch office and enjoy a good proportion of time working from home, if desired. Get the opportunity to occasionally visit our customers' engineering sites and experience first-hand how our work is transforming their ways of working    
• Use first-class equipment for working in-office or remotely, including HPC 

We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics.