Software Engineer - Game AI

Hadean
London
1 year ago
Applications closed

Related Jobs

View all jobs

Software Engineer - Data Engineering

Software Engineer - Data Analytics

Software Engineer - Data Engineering

Data Engineer - DV Cleared

Data Engineer - DV Cleared

Data Engineer


Hadean is an award-winning, record-breaking technology enabler.

Our transformational software is changing the ways we plan, build, learn, work and live.

Through immersive virtual systems, unprecedented connectivity and near unlimited scalability we empower our partners to obtain critical, data-driven insights that enhance operational performance and increase their capabilities to drive productivity in ways that were not possible before.

We are bold in our approach and aren't afraid to push the boundaries of what’s possible.

Hadean’s solutions have helped organisations including Microsoft, The British Army, Minecraft, The MoD, Sony, the Francis Crick Institute and more to achieve groundbreaking performances that have revolutionised their operations.

The Role

We are seeking an experienced Game AI Engineer who is looking to apply their knowledge to a new and exciting challenge. You will work on huge simulations of the real world, proving the power of Hadean’s innovative technology to seamlessly weave together simulated and real-world data into ground-breaking and highly immersive, live training simulations. You will work with our team of infrastructure and distributed computing specialists to take the AI algorithms and tools that you know and demonstrate them at unprecedented scale.

Key Responsibilities

  • Prototype and implement NPC and Bot simulation features and systems (e.g. path finding, movement, goal planning, crowds, swarms)

  • Collaborate with Product and Delivery Managers to refine and design new features

  • Write high quality, performant and maintainable code primarily in C++

  • Participate in peer code and design review

  • Maintain a backlog that balances the timely delivery of features with quality

  • Drive continuous improvement in the engineering team


Skills Knowledge and Expertise

  • Bachelor’s degree in Computer Science, Software Engineering or equivalent experience

  • 5+ years of professional experience working on games or other 3D synthetic environments

  • Experience developing AI games systems or defence or enterprise pattern of life simulations

  • Extensive knowledge of game AI algorithms (e.g. pathfinding, steering, behaviour trees, state machines, animation blend trees, sentiment, knowledge representation)

  • Experience with Unreal Engine, VR-Forces and VBS highly advantageous


Job Benefits

We make Hadean an awesome place to work with competitive benefits…

  • Hybrid working with 1 day per week in our fantastic office in Shoreditch, London

  • Private Health Insurance

  • Enhanced pension scheme

  • Enhanced parental leave

  • 3 extra days off at Christmas (on top of our standard 25)

  • L&D budget

  • Regularly scheduled socials

  • Share options


A Place For Everyone

We believe diversity drives innovation and for that reason we strongly encourage those from all backgrounds to apply for roles at Hadean. We are an equal opportunity employer and aim to build a workforce that is truly representative of the communities in which we operate and our clients.

If you need reasonable adjustments at any point in the application or interview process, please speak with the People team who will be happy to support you. If you have a preferred pronoun, please feel free to highlight this during the process (e.g. she/her, he/him, they/them, etc.).

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.