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About the Role

PhysicsX is seeking individuals to join their deep-tech company, which is accelerating hardware innovation using AI-driven simulation software. The role involves translating engineering challenges into mathematical formulations, building predictive models using machine learning, and collaborating with teams and customers to implement solutions.

Responsibilities

• Work closely with machine learning engineers, simulation engineers, and customers to translate physics and engineering challenges into mathematical problem formulations.
• Build models to predict the behavior 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 internally and externally through publications, workshops, and customer conversations.
• Foster a nurturing environment for colleagues with less experience in DS / ML / Stats for them to grow and mentor.

Requirements

• Enthusiasm for 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 analyze issues, identify causes, and recommend solutions quickly.
• Excellent collaboration and communication skills with teams and customers.
• PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field.
• Expertise in operator learning (neural operators), or other probabilistic methods for PDEs.
• Expertise in geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data.
• Expertise in generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.).
• Over 2 years of experience in a data-driven role.
• Experience 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.
• Experience developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical).
• Experience iterating on network architectures and model structure, tuning and optimizing for inductive biases, improved generalisability, and improved performance.
• Experience combining theoretical reasoning with empirical intuition to guide investigation.
• Experience formulating and running experiment pipelines to benchmark models and produce comparable results.
• Strong writing skills for communicating complex technical concepts to peers and non-peers.
• Publication record in reputable venues demonstrating mastery in the field, particularly in operator learning, geometric deep learning, or generative models.
• Publication in desirable venues including NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR or TPAMI/JMLR.

Skills

• Machine learning
• Deep learning
• Probabilistic methods
• Python
• NumPy
• SciPy
• Pandas
• PyTorch
• JAX
• Operator learning
• Neural operators
• Geometric deep learning
• 3D computer vision
• Generative models
• VAEs
• Diffusion Models
• Bayesian non-parametric
• High-dimensional data analysis
• Spatiotemporal data analysis
• Geometric data analysis
• Network architecture iteration
• Model tuning and optimization
• Inductive biases
• Generalisability
• Performance optimization
• Theoretical reasoning
• Empirical intuition
• Experiment pipeline formulation
• Model benchmarking
• Technical writing
• Academic writing
• Non-academic writing

Location

• Manhattan office
• Remote flexibility

Work Type

• Hybrid
• Remote

Experience Level

• Seniority will be assessed throughout our interview process

Education Level

• PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field

Salary/Compensations

• $120,000 - $240,000 depending on experience

Benefits

• Equity options
• 5% 401(k) match
• Flexible working
• Hybrid setup
• Enhanced parental leave
• Private healthcare
• Personal development
• Work from anywhere
• Free team lunch 1x/week
• 20 days of Annual Leave (+ Public Holidays)
• Gympass / Wellhub (subsidized)
• Flexible Spending Account (FSA)

About the Company

• 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.
• Build what actually matters.
• Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society.
• This is work with real-world impact - and something you can be proud to stand behind.
• Learn alongside exceptional people.
• Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better.
• We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there.
• If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.
• Influence over hierarchy.
• We operate with a flat structure: good ideas win - wherever they come from.
• Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.
• Sustainable pace, long-term ambition.
• Building meaningful technology is a marathon, not a sprint.
• We believe in balancing focused, ambitious work with a life beyond it.
• Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

Equal Opportunity

• 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.
• We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with UK employment and equality legislation.
• This information is confidential, used only in aggregate form, and will not influence the outcome of your application.