At a Glance
- Tasks: Transform research prototypes into robust ML code and build distributed training capabilities.
- Company: Join a pioneering venture-backed company focused on extreme physics and global infrastructure.
- Benefits: Competitive salary up to £180k, high agency in a dynamic environment, and exclusive opportunities.
- Other info: Apply through Dex for exclusive insights and opportunities not openly advertised.
- Why this job: Be the first engineering owner of the ML stack and shape the engineering culture from day one.
- Qualifications: Senior engineer with deep PyTorch experience and a strong background in model engineering.
The predicted salary is between 80000 - 100000 £ per year.
This early-stage, venture-backed company is building foundation models for extreme physics. Think semiconductors, aerospace, defence, and fusion energy. Existing simulation tools are too slow, too brittle, or too expensive to keep pace. Their mission: accelerate progress in fields critical to global infrastructure and energy.
You’ll be the first dedicated engineering owner for the ML stack. This isn’t a narrow systems role, and it isn’t pure research. You’ll join a small, research-heavy team, owning everything from model code and training workflows to experiment infrastructure and repo standards. You’ll also build distributed training capabilities and the backend platform that delivers models to customers.
The work
- Turn research prototypes into robust, production-ready ML code.
- Integrate disparate research branches into a single, coherent codebase.
- Profile and resolve bottlenecks in training, ensuring stability and speed for large-scale models.
- Architect and implement distributed training capabilities from scratch, enabling multi-GPU and multi-node setups.
- Build critical in-house tooling for hyperparameter optimization, ablation, and experiment tracking.
- Own the backend platform that delivers these foundation models to customers.
What You Bring
- You’re a senior, hands-on engineer who still writes and ships critical code, thriving with high agency in an early-stage environment.
- Deep PyTorch experience across model code, data pipelines, training loops, and runtime behavior – not just at the modeling layer.
- Practical experience with distributed training (multi-GPU/node, GPU clusters), including memory, communication, and stability challenges.
- Proven model-engineering background in physics/simulation, vision, or LLM systems, with a track record of improving ML systems through measurement and profiling.
- Strong Python platform and backend foundations: API design, workflow orchestration, and operational tooling (Docker/K8s/IaC).
Why apply through Dex
This is a rare, high-impact role that won’t be openly advertised. Apply through Dex to get properly briefed on the company and team before you interview. We’ll also match you to other exclusive roles like this one, helping you cut through the noise of cold applications and find the real opportunities.
Founding Engineer - ML Systems (up to £180k) employer: Dex
Join a pioneering early-stage venture-backed company at the forefront of building foundation models for extreme physics, where you will have the unique opportunity to shape the engineering culture from day one. With a strong focus on innovation and collaboration, this role offers competitive compensation, a dynamic work environment, and the chance to contribute to critical advancements in global infrastructure and energy. You'll be part of a small, research-heavy team that values your expertise and encourages personal growth, making it an excellent place for those looking to make a meaningful impact.
