openashbyhqhitachiventures
Machine Learning Technical Lead
tem.
LocationUnited Kingdom
WorkplaceRemote
EmploymentFullTime
Posted2026-05-29T13:35:54.709+00:00
Last observed2026-06-13 05:23:28.191314
Job idhitachiventures-tem:ashbyhq:57d0dd82-506d-496a-8463-f93929c5615f
📈 Who We Are: We are rebuilding the energy transaction, making it transparent and fair. Our goal is to put power back where it belongs, in the hands of customers and to take on one of the most critical problems of our century, access to low cost electricity. tem exists to fix a broken global energy market that’s long favoured legacy operators, intermediaries, and opaque pricing. Today’s electricity system was not designed for rapid decarbonisation, AI-driven efficiency or fair access for the actual users - businesses and generators. We’ve built the first AI native transaction infrastructure to reinvent how electricity is bought, sold and priced. Our technology is designed to cut out the inefficient fees, automate complex market flows, and bring transparency and fairness to energy transactions at scale. In late 2025, after extraordinary growth, we closed a $75 million Series B - led by Lightspeed Venture Partners with participation from Albion, Atomico, Allianz, Hitachi Ventures, Hitachi Ventures, Schroders Capital and others - positioning us for global expansion, deeper product innovation and category leadership. We’re scaling internationally and building toward a future where AI-driven infrastructure is foundational to electricity markets worldwide. Since launch, our modern utility product, known as RED, has already facilitated thousands of business customers and billions in energy transaction value, proving that modern software and AI can transform an industry built on legacy systems. At tem, we’re not just building another energy company, we’re rearchitecting market infrastructure so that transparency, efficiency and sustainability become the default, not the exception. 🏅 THE ROLE: We're looking for an Machine Learning Technical Lead to own tem's most technically complex function. Rosso is tem's core IP: the AI-powered engine at the heart of how we price, forecast, and optimise energy transactions. The ML engineers who build it work across time-series forecasting, pricing, optimisation, and classical ML simultaneously. The work is technically complex and commercially critical. This role sits within our Leader track: one person owns their unit end to end - people, strategy, delivery, budget, and outcomes. That's this role. You're not a coach on the sideline. You're the person accountable for the ML function performing and for Rosso hitting its numbers. You'll report into the GM of Rosso, set strategic direction for ML in partnership with them, and work closely with the Rosso Engineering Manager to keep ML and software engineering operating as one team. You'll own the hiring bar, and be directly responsible for the performance and development of every ML engineer in the function. In your first 12 months: the ML engineers trust you and see you as their owner; the function has clear operating rhythms and a predictable hiring pipeline; each engineer has a clear development path; and ML and software engineering collaboration inside Rosso is noticeably stronger. 🚀 RESPONSIBILITIES: - Own the ML function end to end: You hold the people, the priorities, the strategy, and the outcomes. This isn't a coordination role. You're the single accountable leader for how the ML function performs inside Rosso. - Set and sign off on ML strategy: Work with your ML engineers and Experts to develop strategic direction. Propose it, debate it, sign off with the GM. When there's alignment, operate with a high degree of autonomy. - Build a high-performing team: Lead hiring, onboarding, performance management, and career development. Set the frameworks and operating rhythms that give ML engineers clarity, support, and room to grow. Act on underperformance. Hold the hiring bar high as the team scales. - Own the operating systems: Build and maintain the rituals and structures that keep the team effective - sprint cadences, incident review, model monitoring feedback loops, cross-team reporting, and the prioritisation processes that keep the function fo...
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