About the job
The company is an energy startup on a mission to make energy abundant and affordable, fast. We combine first-principles thinking with cutting-edge technology to build a radically better energy system.
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We've raised over $200M from top-tier investors including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird and Collaborative Fund, alongside strategic angels including Nico Rosberg and GPs behind Meta, Revolut, Spotify, and Uber.
We're building a fully integrated energy company: developing our own solar, batteries, and other generation projects, building our own hardware, improving and developing grid infrastructure, trading power in real-time, using AI across the business, and installing distributed energy in homes. By selling directly to consumers, we cut out the middleman, lower costs, and pass the savings on to our customers.
We're building a cutting-edge AI team. As an Applied AI Engineer, this role suits someone who is specifically interested in applied AI and how it can improve the energy experience for our customers and our internal operations.
You'll work on consumer features (using VLM and LLM tools) and build AI tools and agents that make teams across the company more productive, from customer support to warehouses and back office. This is an applied engineering role, not pure research: you'll own what you build, from spec to production.
Responsibilities
- Ship AI features for customers, such as personalised energy recommendations and onboarding from an energy bill
- Build LLM applications and agents for support, engineering, warehouse, and back-office teams, including multi-agent systems and durable workflows (e.g. Temporal)
- Build the engineering around them: cloud infrastructure, integrations, and access control so users and agents only reach what they should
- Own evaluation: datasets, golden answers, human and LLM judges, A/B tests. Know whether a change genuinely helped
- Choose models by performance, latency, and cost, and revisit as models improve
- Work with trading, operations, and backend teams so models reflect real-time market conditions, energy pricing and demand, and weather forecasts
- Monitor AI systems in production and keep them reliable
Requirements
- 3+ years of experience, with requirements varying by role level
- Experience owning an applied AI system end to end, ideally in a startup or small team, and able to explain what broke and how you knew it worked
- A software engineer who moved into applied AI, or an ML engineer who writes production code. We value both
- Strong Python (or similar) and experience deploying LLMs or VLMs into production
- Solid understanding of cloud technologies, containerization, and scalable AI applications
- Analytical, rigorous, and quick to learn. You don't need our exact tools, just the ability to pick them up
- Bonus: PyTorch or TensorFlow; forecasting, weather and demand modeling; energy markets and trading; fine-tuning or RL; vision, document or NLP pipelines; agent runtimes and MCP
Benefits
- Competitive salary and eligibility for equity
- Biannual bonus scheme
- Fully expensed tech to match your needs
- Private health insurance
- Breakfast and dinner allowance for office-based employees
As we hire globally, benefits may vary by location.
