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- Tasks: Design and build AI agent frameworks, ensuring reliable workflows and integrations.
- Company: DeepFlow, a pioneering platform for scaling AI-native businesses.
- Benefits: Up to £120,000 salary, significant equity, and fully remote work.
- Why this job: Join a dynamic team and shape the future of AI with real impact.
- Qualifications: Strong software engineering skills, experience with AI tools, and full-stack capabilities.
- Other info: Fast hiring process and exceptional team with proven success in AI.
The predicted salary is between 100000 - 120000 £ per year.
Location: Remote first (global, UK timezone)
Compensation: Up to £120,000 + significant equity
Type: Full-time
What is DeepFlow?
DeepFlow is the hyperscaling platform. We launch and scale AI-native businesses. An operator with 10 years of domain expertise pairs with DeepFlow’s platform and engineering support to build a business that would previously have needed a team of 50. You’re building the agents that make it work.
Where we are now
- Platform live with paying customers across law, finance, recruitment, and media
- Launching our founding cohort through DeepFlow Foundry: deploying agents into real revenue-generating businesses
- Preparing for a significant raise to scale from 2–3 companies to 10+ by September, with a roadmap to 100 in 2027
- Research team published at AAAI 2026 (invited oral) and DAI 2025, with work adopted by top global universities and companies
The Role
You own the agent layer. Every AI workflow that runs on DeepFlow - the scaffolding, the prompts, the tool integrations, the evaluation pipeline - is your responsibility. Your decisions about agent architecture, provider selection, and quality assurance directly determine whether our companies’ AI delivers in production. This is a software engineering role with an agent specialisation. You’re a full-stack engineer first - you write backend services, build APIs, and ship features end-to-end. Your focus is the model layer, but you’re not siloed to it. You work directly with the CTO/CPO and CSO to shape product direction. Significant equity reflects the scope and impact of the position.
What You’ll Build
- Agent Scaffolding: Designing and building the frameworks that connect LLMs into reliable, production-grade workflows - prompt engineering, tool use, and multi-step chains that hold up at scale.
- Provider Integration: Working with closed-source providers (OpenAI, Anthropic, Google, etc.) via OpenRouter. Managing model selection, fallbacks, cost optimisation, and latency across providers.
- Agent Evaluation: Building the eval infrastructure that measures whether agents actually work - regression testing, quality metrics, and structured failure analysis.
- Integration Engineering: Connecting agents to the external world: Slack, GSuite, CRMs, accounting tools, and domain-specific APIs. The integration layer that makes agents useful in real businesses.
- Full-Stack Delivery: You write backend services, build API endpoints, and contribute to the frontend when needed. Agents don’t exist in a vacuum - this is still a software engineering role.
- Product Direction: You work closely with the founding team to translate cohort feedback and client needs into agent architecture decisions. What to build matters as much as how you build it.
What We’re Looking For
- Strong software engineer first. You write clean, production-quality code and you ship fast. Real deployed systems, not notebooks.
- Experience building with closed-source model providers in production - shipping agent workflows to real users, not just prototyping.
- Practical understanding of prompt engineering, tool use, and agent evaluation. You know what makes an agent reliable versus what makes a good demo.
- Full-stack capable. Backend (Python), infrastructure basics, and enough frontend (React/TypeScript) to ship features end-to-end.
- Fluent with modern AI tooling - you use Cursor, Claude Code, or similar daily and it makes you faster. AI tooling is not optional here.
- Comfortable with ambiguity and startup pace. Priorities shift weekly based on cohort feedback.
- Right to work in the UK permanently.
We care less about
- Your degree (or lack of one)
- How many years you’ve been working - we care about what you’ve shipped
- Whether you’ve published papers - this is an engineering role, not a research role
Tech Stack
- Backend & Agents: Python, pydantic-ai, proprietary agent frameworks, OpenRouter
- Frontend: TypeScript, React, Vite
- Infrastructure: AWS (EKS), Docker, Inngest (event-driven orchestration)
- Tools: Cursor, Claude Code, GitHub
Why Join
- Ownership: Significant equity as a founding engineer. The agents you build power every business on the platform.
- Real traction: Paying customers, founding cohort live, and a significant raise in preparation. What you build in the next 6–12 months directly determines whether we hit 10 companies by September.
- Exceptional team: Co-founder who sold his AI company to WPP for $100M. Product leader who scaled a Meta platform to 400K+ users. Research team published at top AI venues with work adopted by leading universities and Fortune 500 R&D teams. Advisors include a leading NLP researcher and a Google DeepMind team lead.
- Flexibility: Fully remote, UK timezone. Regular offsites. We care about what you ship, not when you’re online.
We’re hiring immediately. The process is fast: we make offers within one week.
Performance Engineer - Sport in London employer: DeepFlow
Contact Detail:
DeepFlow Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Performance Engineer - Sport in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects and contributions. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common questions and coding challenges. Mock interviews with friends or using platforms can help you feel more confident and ready to impress.
✨Tip Number 4
Apply through our website! We love seeing candidates who take the initiative. Tailor your approach to highlight how your skills align with our needs at DeepFlow, especially in AI and full-stack development.
We think you need these skills to ace Performance Engineer - Sport in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Performance Engineer role. Highlight your experience with AI workflows, backend services, and any relevant projects that showcase your skills in building reliable systems.
Showcase Your Code: Include links to your GitHub or any other repositories where we can see your clean, production-quality code. We want to see what you've shipped, so don't hold back on sharing your best work!
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language to explain your experience with prompt engineering and tool integration. We appreciate brevity and clarity, so get to the point!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the quickest way for us to receive your application and get the ball rolling. Plus, it shows you're keen to join our team!
How to prepare for a job interview at DeepFlow
✨Know Your Tech Stack
Familiarise yourself with the tech stack mentioned in the job description, especially Python, TypeScript, and AWS. Be ready to discuss your experience with these technologies and how you've used them in real projects.
✨Showcase Your Full-Stack Skills
Prepare examples that highlight your full-stack capabilities. Discuss specific projects where you’ve built backend services and integrated them with frontend components. This will demonstrate your ability to deliver end-to-end solutions.
✨Understand AI Workflows
Brush up on your knowledge of AI workflows, particularly around prompt engineering and agent evaluation. Be prepared to explain what makes an agent reliable and how you’ve implemented these concepts in past roles.
✨Embrace Ambiguity
Since the role requires comfort with shifting priorities, think of examples from your past where you successfully navigated ambiguity or rapid changes. This will show that you can thrive in a startup environment like DeepFlow.