Asks for

The ad says“software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);”
The ad says“C/C++ for computer vision, geometry processing, or scientific computing;”
The ad says“software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);”
The ad says“C/C++ for computer vision, geometry processing, or scientific computing;”
The ad says“Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering.”
The ad says“container-ization and orchestration (Docker, Kubernetes, Slurm);”
The ad says“C/C++ for computer vision, geometry processing, or scientific computing;”
The ad says“High-performance computing (CPU / GPU clusters);”
The ad says“container-ization and orchestration (Docker, Kubernetes, Slurm);”
The ad says“Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering.”
The ad says“software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);”
The ad says“Parallelised / distributed training for large / foundation models.”
The ad says“Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering.”
The ad says“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;”
The ad says“Scientific computing;”
The ad says“container-ization and orchestration (Docker, Kubernetes, Slurm);”
The ad says“software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);”
The ad says“software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);”

Values

The ad says“cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP);”
The ad says“cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP);”
The ad says“distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);”
The ad says“distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);”
The ad says“scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus);”
The ad says“cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP);”
The ad says“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;”
The ad says“distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);”
The ad says“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;”
The ad says“distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);”
The ad says“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;”
The ad says“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;”
The ad says“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;”
The ad says“distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);”
The ad says“distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);”

About us

Re-architecting Engineering for the Age of Intelligence

PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area.

PhysicsX is starting a research team in Singapore to build physical foundation models alongside our customers and partners, targeting engineering domains where this capability will be most transformative.

What you will do

Work closely with our research scientists, simulation engineers, customers and partners to deliver AI models that address real-world physics and engineering problems.
Design and build physical foundation models with a focus on efficiently scaling model training to large data on multi-GPU cloud compute.
Transform prototypes from your research scientist colleagues into robust and optimised implementations, challenging architecture decisions that hurt scalability.
Identify and argue for the best libraries, frameworks and tools to set us up for success.
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.
Work at the intersection of data science and software engineering to translate the results of our Research into re-usable libraries, tooling and products.
Foster curiosity and initiative among your colleagues and mentees.

What you bring to the table

Enthusiasm about 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 alike.
MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following:
- Scientific computing;
- High-performance computing (CPU / GPU clusters);
- Parallelised / distributed training for large / foundation models.
Ideally, >2 years of experience in a data-driven, professional setting, with exposure to:
- scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus);
- distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton);
- cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP);
- 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;
- C/C++ for computer vision, geometry processing, or scientific computing;
- software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps);
- container-ization and orchestration (Docker, Kubernetes, Slurm);
- writing pipelines and experiment environments, including running experiments in pipelines in a systematic way.

What we offer

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 Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.

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 employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.