This article is designed for students and recent graduates who are exploring graduate career opportunities in artificial intelligence in London. It outlines the top companies currently hiring graduate and entry-level AI engineers, explains the pros and cons of each pathway, and highlights why Permutable AI stands out as an excellent place to begin a career as a graduate AI engineer.
Artificial Intelligence is reshaping the world we live in – powering financial markets, transforming healthcare, redefining media, and even steering self-driving cars. For graduates ready to begin a career in AI engineering, London is a hub of opportunity.
From global tech powerhouses to ambitious startups, the capital offers diverse entry points into the world of AI. But not all graduate roles are created equal. Some provide structured training, others deliver immediate hands-on exposure. At Permutable AI, we are a fast becoming a standout choice for a graduate AI engineer eager to learn, contribute, and grow.
The best companies to join as a Graduate AI Engineer in London
1. Permutable AI – A graduate-friendly launchpad with a chance to grow with and shape our business
At Permutable AI, we are a fast-growing London startup specialising in real-time data intelligence for commodities, energy, and financial trading. Unlike many startups, we have an active graduate recruitment program, offering a rare opportunity for early-career talent to gain hands-on experience in a high-growth environment. At Permutable, graduates are immediately immersed in projects that directly impact financial markets and global trade, ensuring that learning and contribution go hand-in-hand. Additionally, our consistent five star rating on Glassdoor means we are one of the best AI starts up to work for in London.
Pros:
Hands-on impact from day one, working directly on ML, NLP and LLM-based systems.
Broad exposure across the AI stack, from data engineering to deployment.
Meaningful applications in live trading and global decision-making.
Supportive, collaborative culture with close mentorship from senior engineers.
Cons:
Salaries (c. £30–35k) are competitive for startups but lower than big tech (but then we think the opportunities to make a real-impact and our incredible start up culture far outweigh this!)
Fast-paced, high-responsibility environment with steep learning curves.
2. PwC UK – Graduate Technology & AI
PwC offers one of the most established graduate programmes in technology and AI, designed for students who value structured training and professional progression. Graduates rotate across industries and projects, often working directly with clients to implement data-driven solutions. While not every role is a graduate AI engineer position, the programme provides excellent foundations.
Pros:
Highly structured graduate programme with formal training.
Excellent brand recognition for CVs and career advancement.
Mentorship and clear progression pathways.
Cons:
Roles can lean towards analytics, dashboards, and automation rather than advanced ML.
Consulting culture means client work takes precedence over deep technical projects with heavy workloads and late nights.
Large organisational structure may slow individual progression and the feeling that you’re just a cog in a machine not making any real impact.
3. EY (Ernst & Young) – Analytics & AI Graduate Programme
EY’s Analytics & AI graduate programme is a rotational scheme that blends training with real-world client exposure. This is an attractive option for those who want to combine technical learning with consulting skills. Graduates gain experience across industries, working on projects ranging from analytics to AI-driven transformation initiatives, though it may not always feel like a graduate AI engineer track.
Pros:
Structured rotations that expose graduates to a variety of industries.
Professional development opportunities and training.
Global recognition and career mobility.
Cons:
AI exposure can be limited compared to pure ML-focused companies.
Consulting workload can be demanding and long hours and late nights are common.
May involve more data wrangling than AI model development.
4. Wayve – Autonomous Driving
Wayve is one of London’s most innovative AI startups, pioneering the use of deep learning to create self-driving technology. Its work sits at the frontier of computer vision and robotics. For a graduate AI engineer with a strong research background, Wayve offers a highly technical environment, though competition is fierce.
Pros:
Cutting-edge research in deep learning and computer vision.
Opportunity to contribute to world-changing technology.
Highly technical environment with strong research culture.
Cons:
Entry is extremely competitive, often requiring advanced degrees or research experience.
Narrow technical focus compared to broader applied AI roles.
Less beginner-friendly due to high entry requirements.
5. Synthesia – Generative AI for Video
Synthesia is a high-profile London startup leading the way in AI-generated video. The company is known for its rapid growth and strong media presence. For a graduate AI engineer, this offers a chance to enter the booming generative AI sector, though entry-level roles may be more product- or operations-focused.
Pros:
Strong brand visibility in generative AI.
Fast-growing sector with exciting product applications.
Exposure to a leading startup environment.
Cons:
Roles may not be heavily technical or focused on ML research.
High growth means priorities can shift rapidly.
Competition is strong due to the company’s reputation.
6. PolyAI – Conversational AI
PolyAI develops advanced conversational AI systems for enterprise use, specialising in voice assistants and customer interaction. For graduates interested in NLP, this offers strong applied focus, though it may not be the easiest entry point for a graduate AI engineer without prior NLP experience.
Pros:
Strong applied focus in conversational AI and NLP.
Opportunity to work on customer-facing products with real-world impact.
Scale-up environment combining agility with stability.
Cons:
Graduate intake is smaller and less structured than at larger firms.
Prior NLP or language model experience often expected.
Narrower focus on conversational AI compared to broader AI roles.
7. Gradient Labs – AI for Regulated Industries
Gradient Labs is a relatively new startup founded by ex-Monzo engineers, focused on building AI solutions for regulated industries like finance. Its innovative approach to agentic AI makes it an exciting prospect for entrepreneurial graduates, though being at such an early stage means less structure for a graduate AI engineer.
Pros:
Exposure to cutting-edge agentic AI.
Innovative startup with strong founding team.
Early career employees can take on significant responsibility.
Cons:
Very early stage, meaning less job security and limited training.
Less structured graduate pathways compared to consultancies or big tech.
High variability in workload and focus areas.
8. Unitary – AI Content Moderation
Unitary builds multimodal AI systems to moderate harmful online content. This is a socially impactful area of AI, growing rapidly with the rise of online safety regulation. While exciting, roles may require niche expertise that can be challenging for a new graduate AI engineer.
Pros:
Work on socially impactful problems around online safety.
Exposure to multimodal AI systems (video, audio, text).
Growing demand for AI in content moderation.
Cons:
Roles may require specialised skills early on.
Graduate training less formalised.
Narrower focus on moderation rather than broad AI applications.
9. Quantexa – Graph and ML for Risk & Fraud
Quantexa is a well-funded enterprise AI company tackling fraud and financial crime with graph analytics and ML. It offers stability, strong commercial applications, and structured teams, making it a pragmatic choice for a graduate AI engineer interested in financial services.
Pros:
Clear, commercially relevant applications in fraud detection.
Structured teams and established processes.
Strong funding and long-term growth prospects.
Cons:
Work may involve more data engineering and integration than deep ML research.
Enterprise culture may feel corporate compared to startups.
Graduate roles may be more narrowly defined.
10. Faculty – Applied AI Consultancy
Faculty is one of the UK’s best-known AI consultancies, with projects spanning government, defence, and industry. Its structured graduate schemes make it one of the more accessible ways to begin as a graduate AI engineer, even if not all roles are purely technical.
Pros:
Established graduate programme with structured training.
Wide exposure to government and commercial AI projects.
Strong reputation in the UK AI sector.
Cons:
Consultancy-heavy environment means not all work is deep technical AI.
Scope of projects can vary widely.
Work may involve balancing technical delivery with client management.
11. Big Tech / Research Giants (Google DeepMind, Microsoft, Amazon)
London hosts some of the world’s most prestigious AI research centres, including Google DeepMind. For a graduate AI engineer, these roles offer the opportunity to work with world-class resources, though they are extremely competitive and often demand advanced qualifications.
Pros:
Access to world-class research facilities and infrastructure.
Attractive salaries and benefits.
Opportunity to work at the cutting edge of AI research.
Cons:
Extremely competitive recruitment, often requiring MSc or PhD.
Roles can be highly specialised and narrow in scope.
Bureaucracy may slow personal impact.
Final thoughts
London remains Europe’s leading hub for AI, offering a wide spectrum of opportunities. Structured consultancies, cutting-edge labs, and agile startups each offer unique benefits to graduates.
But for those who want to make an impact from day one, learn across the full AI stack, and see their work applied directly to global markets, we firmly believe that Permutable AI is one of the best choices for a graduate AI engineer.
Take the first step in your AI journey and explore opportunities at Permutable AI Careers and over on 2.