Machine Learning Engineer (Physics AI)

DeepRec.ai; Greater London; Ibrido; GBP 100.000 - 140.000; Tempo pieno

Descrizione del lavoro

DeepRec.ai in Central London is seeking a Machine Learning Engineer to join an early-stage, VC-backed startup building physics-based AI models. You’ll work across research and engineering on a generative physics simulation platform with a strong emphasis on real-world impact.

The role focuses on designing and training models for physics simulations, optimizing inference speed, and collaborating with engineering teams to deploy production pipelines.

Competenze

• Experience designing and training ML models for physics simulation.
• Ability to deploy models in production-grade pipelines.
• Strong grounding in ML theory: optimisation and generalisation.
• Experience with geometry or physical systems is a plus.

Mansioni

• Design and train deep learning models for physics simulation across aerodynamic and engineering domains
• Drive optimisation of model inference speed, accuracy and robustness on large-scale industrial datasets
• Research effective ways to represent geometric design variation for use by ML models
• Partner with engineering teams to deploy and monitor models in production-grade pipelines
• Contribute to decisions on model and data architecture, tooling and ML infrastructure
• Strong track record applying ML to complex real-world problems, ideally involving geometry or physical systems
• Deep grounding in ML theory - optimisation, generalisation, model architectures
• Strong Python skills and hands-on experience with PyTorch, TensorFlow or JAX
• Ability to explain complex ML concepts clearly to technical and non-technical audiences
• Master's degree in ML, Computer Science or a related quantitative field (PhD preferred)

Conoscenze

Python

ML theory

Model deployment

Explain ML concepts

Strong communication

Formazione

Master's degree in ML/CS or related field

Strumenti

PyTorch

TensorFlow

JAX

Descrizione del lavoro

Machine Learning Engineer | Central London (3 days/week) | £100k–£140k + Equity DOE

We're working with a VC-backed deep tech startup building foundational AI models for physics - replacing slow, expensive numerical simulation with AI that matches traditional accuracy at orders of magnitude faster speed. The problems are real-world and hard: aerodynamics, CFD, electromagnetics and mechanics, applied across automotive, aerospace and energy - sectors still running on decades-old simulation tooling that’s ripe for disruption. They've just closed a strong pre-seed round backed by top-tier VCs and are assembling an early team alongside veterans from world-leading AI labs and engineering firms, working directly with industry partners rather than in the abstract.

As Machine Learning Engineer, you'd sit right at the centre of their Generative Physics simulation platform, working across both research and engineering.

Responsibilities

• Design and train deep learning models for physics simulation across aerodynamic and engineering domains
• Drive optimisation of model inference speed, accuracy and robustness on large-scale industrial datasets
• Research effective ways to represent geometric design variation for use by ML models
• Partner with engineering teams to deploy and monitor models in production-grade pipelines
• Contribute to decisions on model and data architecture, tooling and ML infrastructure
• Strong track record applying ML to complex real-world problems, ideally involving geometry or physical systems
• Deep grounding in ML theory - optimisation, generalisation, model architectures
• Strong Python skills and hands-on experience with PyTorch, TensorFlow or JAX
• Ability to explain complex ML concepts clearly to technical and non-technical audiences
• Master's degree in ML, Computer Science or a related quantitative field (PhD preferred)

Highly desirable

• Familiarity with aerodynamics or CFD
• Experience with optimisation algorithms in an engineering design context
• Experience integrating physical laws or constraints into ML models
• A direct seat at the table shaping a company aiming to redefine an entire industry
• Work that feeds directly into the transition to sustainable energy and more efficient transport
• A small, high-calibre team culture built around "impact with integrity"