Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

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. You will build pipelines that fuse inspection imagery with equipment operating history and physics-based inputs to predict remaining component life, helping operators reduce costly unplanned interventions and optimize asset availability.

Responsibilities

Lead the development of physics-informed predictive maintenance systems for high-value industrial equipment.
Build pipelines that fuse periodic inspection imagery with equipment operating history and physics-based inputs to predict remaining component life.
Translate physical intuition into modeling decisions.
Produce outputs that engineers can trust and operators can act on.
Architect and ship reliable, scalable pipelines deployable within enterprise cloud environments.
Co-develop solutions on-site with customers.

Requirements

Strong research foundation in computer vision and applied deep learning.
Ability to take research foundations into production on real-world industrial problems.
Ability to build models that work under difficult conditions (sparse data, inconsistent image quality, high stakes).
Ability to move fluidly between designing architecture and debugging data pipelines.
Comfortable working directly with domain experts and customers.
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.
Genuine curiosity about engineering domains and asset-intensive operations is essential.

Skills

Computer vision
Applied deep learning
Selecting, adapting, and fine-tuning pretrained vision encoders (ViT, DINOv3, ResNet-family or equivalent)
Gradient-based interpretability methods (Grad-CAM, integrated gradients)
Producing sensitivity maps
Building joint models that combine visual features with heterogeneous inputs (tabular metadata, time-series operating history, physics-derived signals)
Probabilistic or Bayesian output modelling
Calibrated uncertainty quantification
Production ML mindset
Physics-informed or hybrid ML approaches

Location

On-site with customers

Work Type

Full-time

Experience Level

Senior
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

Education Level

PhD or equivalent research experience in computer vision, machine learning, or a related field strongly preferred

Salary/Compensations

$120,000 - $240,000 depending on experience

Benefits

Equity options
5% contribution to 401(k)
Free team lunch 1x/week
Private health insurance
Enhanced parental leave (3 months full pay paternity and 6 months full pay maternity leave)
20 days of Annual Leave (+ Public Holidays)
Personal development support
Gympass / Wellhub (subsidized)
Flexible Spending Account (FSA)

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 — empowering engineers to push the boundaries of possibility.
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.