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- Tasks: Design and evolve LLM evaluation frameworks for cutting-edge AI systems.
- Company: Join an early-stage AI company focused on innovative agentic systems.
- Benefits: Enjoy equity, 30 days holiday, and hybrid working options.
- Other info: Great opportunity for career growth into a Head of AI position.
- Why this job: Shape the future of AI while working closely with founders in a fast-paced environment.
- Qualifications: 2-5 years in backend or software engineering, plus 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 (hybrid or remote) employer: DeepRec.ai
Contact Detail:
DeepRec.ai Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (hybrid or remote)
✨Tip Number 1
Network like a pro! Reach out to people in the AI and machine learning space, especially those who work at companies you're interested in. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to LLMs or agent systems. This gives you a chance to demonstrate your hands-on experience and problem-solving abilities.
✨Tip Number 3
Prepare for the technical deep dive! Brush up on your engineering fundamentals and be ready to discuss your past experiences with production AI systems. They’ll want to see how you think on your feet.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the team.
We think you need these skills to ace Machine Learning Engineer (hybrid or remote)
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 and AI engineering experience, especially any work with LLM applications or agentic systems.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're excited about this position and how you can contribute to our team. Mention specific projects or experiences that demonstrate your ownership mentality and ability to thrive in fast-moving environments.
Showcase Your Technical Skills: In your application, don’t shy away from detailing your technical expertise. Discuss your experience with eval frameworks, APIs, and any architectural decisions you've made in past roles. We want to see how you think and approach problems!
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’re considered for the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at DeepRec.ai
✨Know Your Stuff
Make sure you brush up on your machine learning fundamentals and the specific technologies mentioned in the job description. Be ready to discuss your past experiences with LLM applications and agentic systems, as well as any relevant projects you've worked on.
✨Show Your Ownership Mentality
This role requires a strong sense of ownership, so be prepared to share examples of how you've taken charge of projects in the past. Discuss how you’ve navigated ambiguity and built systems from the ground up, highlighting your problem-solving skills.
✨Prepare for Technical Discussions
Expect to dive deep into technical architecture and evaluation frameworks during the interview. Familiarise yourself with common architectural decisions around latency and performance, and be ready to discuss trade-offs you've made in previous roles.
✨Engage with Founders
Since you'll be working closely with the founders, show genuine interest in their vision for the company. Prepare thoughtful questions about their product direction and how you can contribute to shaping the AI engineering function as they scale.