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About the Role

PhysicsX is seeking individuals to join their Research group, focusing on shaping strategy, defining technical direction, and delivering AI-driven simulation software for advanced industries. This role involves working at the intersection of data science and software engineering to translate research into practical solutions, mentor colleagues, and contribute to the company's mission of accelerating hardware innovation.

Responsibilities

Shape Research group strategy and culture, especially in domains of expertise.
Formulate strategy on engineering topics relevant to Research priorities, including scaled engineering, compute security, and infrastructure stack.
Define necessary profiles to execute the strategy.
Promote effective working patterns and proactively flag issues with team dynamics.
Nurture younger colleagues to grow their skillset and guide their professional development.
Own Research work-streams at a high-level to deliver outcomes.
Align priorities with problem stakeholders, internal and external.
Set the technical direction for the stream and apply judgement to drive progress.
Plan roadmaps with clear milestones.
Organise and guide junior team members to execute against the roadmap.
Communicate purpose and key outcomes across the company.
Work closely with research scientists and simulation engineers to build and deliver models.
Design, build, and optimise machine learning models with a focus on scalability and efficiency.
Transform prototype model implementations to robust and optimised implementations.
Implement distributed training architectures for multi-node/multi-GPU training.
Explore federated learning capacity using cloud and on-premise services.
Work with research scientists to design, build, and scale foundation models for science and engineering.
Identify the best libraries, frameworks, and tools for modelling efforts.
Discuss results and implications of work with colleagues and customers.
Translate research results into re-usable libraries, tooling, and products.
Foster a nurturing environment for colleagues with less experience in ML/Engineering.

Requirements

Enthusiasm for 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.
MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field.
Record of experience in scientific computing.
Record of experience in high-performance computing (CPU / GPU clusters).
Record of experience in parallelised / distributed training for large / foundation models.
4 years of experience in a data-driven role in a professional industry setting.
Experience scaling and optimising ML models, training and serving foundation models at scale.
Experience employing distributed computing frameworks (e.g., Spark, Dask).
Experience employing high-performance computing frameworks (MPI, OpenMP, CUDA, Triton).
Experience employing cloud computing on hyper-scaler platforms (e.g., AWS, Azure, GCP).
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 building or using C/C++ for computer vision, geometry processing, or scientific computing.
Experience following and promoting software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps).
Experience container-izing and orchestrating compute tasks (Docker, Kubernetes, Slurm).
Experience writing pipelines and experiment environments, including running experiments in pipelines in a systematic way.

Skills

Deep learning
Probabilistic methods
Machine learning
AI
Simulation software
Data science
Software engineering
Scientific computing
High-performance computing
Distributed training
Foundation models
Distributed computing frameworks
Cloud computing
Python
NumPy
SciPy
Pandas
PyTorch
JAX
C/C++
Computer vision
Geometry processing
Software engineering best practices
Versioning
Testing
CI/CD
API design
MLOps
Containerization
Orchestration
Docker
Kubernetes
Slurm
Federated learning

Location

Shoreditch office

Work Type

Hybrid

Experience Level

4 years of experience in a data-driven role
MSc or PhD

Education Level

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

Benefits

Equity options
10% employer pension contribution
Free office lunches
Enhanced parental leave (3 months full pay paternity, 6 months full pay maternity)
YellowNest nursery scheme
25 days of Annual Leave (+ Public Holidays)
Private medical insurance (100% employee cover)
Wellhub Subscription
Eye tests
Personal development support
Employee Assistance Programme (EAP)
Bike2Work scheme
Season ticket loan
Octopus EV salary sacrifice

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.
PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations by enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle.
Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive.

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.
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.