Senior Computer Vision Data Scientist

About us 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. Senior Data Scientist — Computer Vision & Predictive Maintenance We are looking for a Senior Data Scientist with deep computer vision expertise to lead the development of physics-informed predictive maintenance systems for high-value industrial equipment. Working directly with major industrial customers, you will build pipelines that fuse periodic inspection imagery with equipment operating history and physics-based inputs to predict remaining component life — helping operators reduce costly unplanned interventions and optimise asset availability.

Who we're looking for You have a strong research foundation in computer vision and applied deep learning, and you know how to take that foundation into production on real-world industrial problems. You are energised by the challenge of building models that actually work under difficult conditions — sparse data, inconsistent image quality, and high stakes for getting it wrong. You can move fluidly between designing the right architecture and debugging a broken data pipeline.

You are comfortable working directly with domain experts and customers to translate physical intuition into modelling decisions, and you care about producing outputs that engineers can trust and operators can act on. What you bring Deep expertise in selecting, adapting, and fine-tuning pretrained vision encoders (ViT, DINOv3, ResNet-family or equivalent) for industrial or scientific imaging problems where data is sparse and capture conditions are variable Strong command of gradient-based interpretability methods (Grad-CAM, integrated gradients) and the ability to produce sensitivity maps that make model behaviour reviewable by non-ML domain experts Proven ability to build joint models that combine visual features with heterogeneous inputs including tabular metadata, time-series operating history, and physics-derived signals Hands-on experience with probabilistic or Bayesian output modelling and calibrated uncertainty quantification Production ML mindset - you architect and ship reliable, scalable pipelines deployable within enterprise cloud environments, not just notebooks Comfort with messy, real-world industrial datasets: sparse time series, noisy labels, irregular collection intervals, and data linkage challenges Strong communication skills across technical and non-technical audiences, including the ability to co-develop solutions on-site with customers PhD or equivalent research experience in computer vision, machine learning, or a related field strongly preferred Experience with physics-informed or hybrid ML approaches is a significant plus Domain experience in aerospace, energy, heavy industry, mining, or MRO is helpful but not required — genuine curiosity about engineering domains and asset-intensive operations is essential And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 💰 5% contribution to 401(k) - build long-term security with a strong retirement plan.

🍽️ Free team lunch 1x/week - good food, great company, and space to connect. 🏥 Private health insurance – comprehensive cover for you, offering total peace of mind. 👶 Enhanced parental leave – 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.