Research Scientist

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.

What you bring to the table

• 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:
1. operator learning (neural operators), or other probabilistic methods for PDEs;
2. geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data;
3. 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., NumPy, SciPy, 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 optimizing 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.

What we offer

• 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 – comprehensive coverage for you and your family.
• Personal development – access learning and training to help you grow.
• Work from anywhere – extend your remote setup to enjoy the sun or reconnect with loved ones.