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Research Scientist

Job in New York, New York County, New York, 10261, USA
Listing for: PhysicsX
Full Time position
Listed on 2026-01-12
Job specializations:
  • IT/Tech
    Data Scientist, Machine Learning/ ML Engineer, Artificial Intelligence
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Location: New York

Join to apply for the Research Scientist role at PhysicsX
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About Us

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.
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:
    • 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.
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 –…
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