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- Tasks: Design and evolve LLM evaluation frameworks for cutting-edge AI systems.
- Company: Early-stage AI company focused on innovative agentic systems.
- Benefits: 0.5% equity, 30 days holiday, hybrid working, and growth opportunities.
- Other info: Fast-paced environment with potential to become Head of AI.
- Why this job: Shape the future of AI engineering and work directly with founders.
- Qualifications: 2-5 years in backend/software engineering and hands-on AI experience.
The predicted salary is between 60000 - 80000 £ per year.
We’re working with an early-stage AI company building production-grade agentic systems and workflow automation products. They are now hiring an AI Engineer to take ownership of their evaluation infrastructure and help shape the future direction of their AI capability. This is not a pure research role or prompt engineering position. The focus is production AI systems, eval frameworks, agent orchestration, and engineering reliability.
You will work directly with founders and engineers to evolve their existing V1 eval framework into a scalable, production-ready V2 system integrated into deployment workflows and engineering pipelines. The environment is fast moving, highly technical, and suited to engineers who enjoy ownership, ambiguity, and building systems end to end.
What You’ll Work On
- Designing and evolving LLM evaluation frameworks for production systems
- Building eval infrastructure directly into deployment and engineering pipelines
- Improving agent reliability, reasoning quality, and orchestration logic
- Defining prompting strategies, sub-agent interactions, and reasoning trade-offs
- Making architectural decisions around latency, reasoning depth, performance, and reliability
- Working closely with founders on product and technical direction
- Helping shape the long-term AI engineering function as the company scales
What They’re Looking For
- 2 to 5 years of backend or software engineering experience
- 1 to 2 years of hands-on AI engineering experience in production environments
- Experience deploying LLM applications or agentic systems into production
- Strong engineering fundamentals across APIs, backend systems, infrastructure, and architecture
- Experience designing evals, benchmarking systems, or AI testing workflows
- Ability to translate business requirements into measurable evaluation frameworks
- Comfortable discussing production failures, trade-offs, and engineering decisions
- Strong ownership mentality and ability to operate in fast-moving environments
Nice To Have
- Experience from start-ups or YC-style environments
- Exposure to multi-agent systems or orchestration frameworks
- Experience integrating eval tooling into CI/CD or deployment systems
- Customer-facing or stakeholder-facing exposure
- Side projects or experimentation around evals, benchmarking, or agent systems
Package
- 0.5% equity
- 30 days holiday plus bank holidays
- Hybrid working in Clapham Junction
- Opportunity to grow into a future Head of AI position as the company scales
Interview Process
- Introductory call
- Founder meeting
- Technical architecture and eval discussion
- Scenario-based onsite session
Machine Learning Engineer employer: DeepRec.ai
Contact Detail:
DeepRec.ai Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and machine learning space, especially those who work at companies you're interested in. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to LLMs or agent systems. Whether it's GitHub repos or a personal website, let your work speak for itself and make it easy for potential employers to see what you can do.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your past experiences with production AI systems. Practise explaining complex concepts in simple terms – it shows you really understand your stuff.
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in joining us. Tailor your application to highlight how your experience aligns with the role, and don’t forget to mention any side projects that relate to eval frameworks or agent orchestration.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Machine Learning Engineer role. Highlight your backend or software engineering experience, especially in production environments, to show us you’re the right fit.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how your background makes you a great candidate for this position. Share specific examples of your work with LLM applications or agentic systems to grab our attention.
Showcase Your Projects: If you've worked on any side projects or experiments related to evals, benchmarking, or agent systems, make sure to mention them! We love seeing hands-on experience and creativity in action.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates during the process!
How to prepare for a job interview at DeepRec.ai
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technical aspects of machine learning and AI engineering. Brush up on your knowledge of LLM evaluation frameworks, agent orchestration, and production systems. Being able to discuss these topics confidently will show that you’re ready to take ownership of the role.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've tackled challenges in production environments. Think about times when you improved system reliability or made architectural decisions. This will demonstrate your ability to operate in fast-moving environments and handle ambiguity.
✨Understand the Company’s Vision
Research the company’s goals and how they plan to evolve their AI capabilities. Be ready to share your thoughts on their current evaluation framework and suggest ways it could be improved. This shows that you’re not just interested in the job, but also in contributing to the company's future.
✨Prepare for Scenario-Based Questions
Since the interview includes scenario-based discussions, practice responding to hypothetical situations related to AI engineering. Think about how you would approach issues like production failures or trade-offs in reasoning depth. This will help you articulate your thought process clearly during the interview.