Research Scientist
Listed on 2026-01-12
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IT/Tech
Data Scientist, Machine Learning/ ML Engineer, Artificial Intelligence
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Physics
X 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, Physics
X 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, however please only apply for the role that best aligns with your skillset and career goals.
What You Will Do- Work closely with our machine learning engineers, simulation engineers, and customers to translate physics and engineering challenges into mathematical problem formulations.
- Build models to predict the behaviour of physical systems using state-of-the-art machine learning and deep learning techniques.
- 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.
- Collaborate with colleagues beyond the research team to translate your models into production-ready code.
- Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. This will involve writing for academic and non-academic audiences.
- Foster a nurturing environment for colleagues with less experience in DS / ML / Stats for them to grow and you to mentor.
- Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering.
- Ability to scope and effectively deliver projects.
- 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.
- PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following:
- operator learning (neural operators), or other probabilistic methods for PDEs;
- geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data;
- generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.).
- Ideally, >2 years of experience in a data-driven role, with exposure to:
- building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., Num Py, Sci Py, Pandas, PyTorch, JAX), especially including deep learning applications;
- developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical);
- iterating on network architectures and model structure, tuning and optimising for inductive biases, improved generalisability, and improved performance;
- combining theoretical reasoning with empirical intuition to guide investigation;
- formulating and running experiment pipelines to benchmark models and produce comparable results;
- writing skills for communication complex technical concepts to peers and non-peers, tailoring the message for the required audience.
- Publication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to):
NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR or TPAMI/JMLR.
- Equity options – share in our success and growth.
- 5% 401(k) match – invest in your future.
- Flexible working – balance your work and life in a way that works for you.
- Hybrid setup – enjoy our Manhattan office while keeping remote flexibility.
- Enhanced parental leave – support for life’s biggest milestones.
- Private healthcare –…
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