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Solve Intelligence

AI Engineer

Solve Intelligence is hiring an AI Engineer in London, United Kingdom. It pays $100k-$250k a year and Level rates it ; you can apply on Solve Intelligence.

Level's read on this job

  • On Level's scale, this AI engineer role is Builds AI ●●●●: the job is building AI systems.
  • Solve Intelligence mentions visa sponsorship in this posting.
  • Tools and skills the posting cites include Perplexity, Hugging Face and prompt engineering.
  • Level lists 17 other open roles at Solve Intelligence.

AI in this role

PerplexityHugging Face
prompt-engineeringai-agents

Want to build AI agents that thousands of patent professionals rely on every day? We're a fast-growing, Series B startup putting state-of-the-art AI in the hands of legal teams across 6 continents, looking for engineers who want real ownership and real impact.

This is your chance to join a rocket ship! 🚀

🏗️ The Role: AI Engineer

We are looking for super-talented AI engineers to help us build state-of-the-art AI agents, extremely fast. You'll be joining the most lean, talent-dense, and fast-moving engineering team in London. You'll be responsible for:

  • Developing production-ready AI agents. We've built proprietary agents and tools for drafting, analysing, and evaluating patents. You'll improve these agents and ship new ones that directly move product quality and customer adoption.

  • Context management, tools and prompt engineering. You'll ensure our AI manages its context effectively at scale, reliably handling thousands of documents while producing the highest-quality output. You'll build the specialist tools and integrations that make this possible, and engineer prompts alongside our IP experts to maximise performance.

  • Building a state-of-the-art evaluation pipeline. Robust evals keep AI output high-quality and objectively steer our R&D. You'll build this pipeline and benchmark performance across large datasets.

👤 About you

As an AI engineer, you will have extensive experience building AI agents and products to solve impressively difficult problems through past projects, designs, or GitHub contributions.

✏️ Essential requirements

  • Expert in Python.

  • Industry experience of shipping agents and/or evaluation pipelines into production.

  • Experience with context engineering and prompt engineering

  • Familiarity with back-end development and Postgres databases.

  • Experience with front-end development (Typescript/React).

  • Extremely clear writer and communicator.

Nice to have:

  • Publications at top machine learning conferences (e.g. NeurIPS, ICLR, ICML) or in adjacent fields.

💰 What we offer

Competitive base salary depending on skill and experience.

Significant equity — we want you to take ownership in Solve Intelligence and share in the success that lies ahead.

Full visa sponsorship.

Opportunity to work with a super smart and ambitious group of people building something people want.

Private medical insurance.

Free meals.

🚀 About Solve Intelligence

Solve Intelligence is the fastest-growing startup transforming the IP landscape. Solve's AI platform manages everything from invention harvesting and patent generation through to litigation and licensing.

Growth: 20–30% MoM revenue growth; selling to 600+ global IP teams across 6 continents (DLA Piper, tech giants, boutiques, etc); 8 figures of ARR — all just ~2 years since we started.

Backing: $55M backing from YC, 20VC, Microsoft, Thomson Reuters, and the founders of top companies like Stripe, Perplexity, Tinder, Deel, Hugging Face, and Canva.

Impact: 50–90% efficiency and quality improvements.

See our latest Series B announcement.

We're on a high-growth trajectory, and we're looking for people who want to join us on the journey to building a generational AI company.

👥 Meet the founders

Sanj (Chief Research Officer and co-founder): Sanj did his MSci in Physics at the University of Cambridge and his PhD in AI at the Gatsby Unit, UCL. He won awards at top AI conferences, worked on the R&D team at Huawei, and received widespread attention for his contributions to open-source LLM projects on GitHub. He joined Magic Carpet AI as employee #4, where he developed AI strategies for trading, before the company was acquired.

Chris (Chief Executive Officer and co-founder): Chris did his undergrad at Imperial College London, his masters at the University of Cambridge, and his PhD in machine learning at UCL, during which he published 10+ papers and won awards at top AI conferences and journals. Prior to founding Solve, Chris worked at The Alan Turing Institute, InstaDeep, and Dyson.

Angus (Chief Technology Officer and co-founder): Angus did an MEng in Computer Science at the University of Bristol where he won a cyber security scholarship at GCHQ before working at Qualcomm. He then joined a London-based hedge fund, Coremont (Brevan Howard). There, he led a small team to build a greenfield trade processing platform which scaled to millions of trades per day.

Ready to build the future of AI? Apply now. 🚀

How we rate this

AI Engineer at Solve Intelligence rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringAI agentsPerplexityHugging Face

Also common in AI engineer jobs at this level

Pay

This job starts at
$100k
Typical AI engineer job
$184k

This posting starts 46% below the typical AI engineer job. See AI engineer jobs

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. What are the limits of Perplexity that you've run into, and how did you work around them?
  4. What's a project where you used Hugging Face hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: Prompt engineering, AI agents, Perplexity, and Hugging Face. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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