Data Scientist (AI/ML)

TL;DR

Data Scientist (AI/ML) (Deep Learning/Physics): Building AI-driven models and simulation software for physical systems in engineering and manufacturing with an accent on deep learning, probabilistic methods, and high-dimensional scientific data. Focus on designing neural operators, geometric and generative models, running rigorous experiments, and translating research into production-ready solutions.

Location: Hybrid in London, United Kingdom, with time together in the Shoreditch office and work-from-home days.

Company

hirify.global is a deep-tech company building an AI-driven simulation software stack for engineering and manufacturing across aerospace, materials, energy, semiconductors, and automotive industries.

What you will do

• Translate physics and engineering challenges into mathematical problem formulations with machine learning, simulation, and customer teams.
• Build predictive models for physical systems using advanced machine learning and deep learning techniques.
• Own research workstreams according to experience level and communicate results to technical and non-technical audiences.
• Develop experiment pipelines to benchmark models and investigate performance, generalisability, and inductive biases.
• Collaborate beyond the research team to convert models into production-ready code.
• Mentor colleagues with less experience in data science, machine learning, and statistics.

Requirements

• PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field.
• More than 2 years of professional industry experience in a data-driven role, excluding postdoctoral positions.
• Experience building machine learning models and pipelines in Python with libraries such as NumPy, SciPy, Pandas, PyTorch, or JAX.
• Experience with high-dimensional spatiotemporal, geometric, or physical data and deep learning applications.
• Strong problem-solving, project delivery, collaboration, and technical communication skills.
• Publication record in reputable venues demonstrating expertise in relevant research areas.

Nice to have

• Expertise in operator learning, neural operators, or probabilistic methods for partial differential equations.
• Experience with geometric deep learning or 3D computer vision for point-cloud or mesh-structured data.
• Experience with generative models for geometry and spatiotemporal data, including VAEs, diffusion models, or Bayesian non-parametric methods.
• Publications in venues such as NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, SIGGRAPH, CVPR, TPAMI, or JMLR.

Culture & Benefits

• Flat structure where ideas are valued regardless of hierarchy, with a focus on meaningful real-world impact.
• Collaborative environment with engineers, scientists, and operators working at a sustainable pace.
• Equity options and a 10% employer pension contribution.
• 25 days of annual leave plus public holidays, private medical insurance, enhanced parental leave, and a nursery scheme.
• Free office lunches, Wellhub subscription, employee assistance support, and personal development opportunities.
• Bike2Work scheme, season ticket loan, and electric vehicle salary-sacrifice options.