Staff Machine Learning Engineer, tem

• Strong optimisation background: experience with linear programming, operations research, or constrained optimisation in real-world systems
• Hands-on ML / data science experience: proven ability to build and ship models that matter
• Production mindset: experience designing, deploying, and maintaining cloud-based ML or optimisation systems
• First-principles thinking: comfortable working in ambiguous, greenfield problem spaces
• Strong Python skills and experience with the standard data science stack
• Commercial awareness: understands how technical decisions translate into business impact
• We welcome applications from people of all backgrounds, experiences, and identities, including those that are traditionally underrepresented in the tech and energy sectors. If you’re excited about this role but not sure you meet every requirement, we’d still love to hear from you. Your unique perspective could be exactly what we’re looking for

Desirable

• We’re looking for a Staff Machine Learning Engineer to play a key hands-on role in developing Rosso, our proprietary pricing and allocation engine, and adjacent optimisation systems such as P442 matching and Red Score logic
• This role is focused on first-principles modelling, optimisation, and production-grade ML systems, with real commercial impact
• You’ll work on greenfield problems where there is limited precedent, helping bring core optimisation IP fully in-house and into production - reducing dependency on third parties and shaping how energy markets operate at scale
• Own and build core optimisation systems: Design, implement, and operate ML and optimisation models that power pricing, matching, and allocation within Rosso and related systems, from research through to production
• Solve complex applied problems: Develop linear programming and optimisation solutions for batch and near–real-time use cases, balancing cost, risk, portfolio constraints, and commercial outcomes
• Ship production-grade models: Build and maintain end-to-end ML and optimisation pipelines in the cloud (AWS preferred), ensuring robustness, explainability, and operational reliability
• Develop core IP in-house: Replace third-party logic with high-quality internal implementations, iterating quickly as product and market understanding evolves
• Collaborate and influence: Work closely with product, engineering, and commercial teams to translate business needs into effective technical solutions, communicating clearly with non-technical stakeholders
• Raise engineering standards: Contribute to best practices in modelling, experimentation, and code quality, and provide informal mentorship to junior engineers and data scientists

• Our processes normally take around 3-4 weeks from first call to offer - please let us know about any adjustments to timelines that may be required
• First call with our Talent Team (30 min). This is to understand your experience, motivations, and discuss the role in more detail
• Behaviour Interview with our Head of Data (45 min). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working
• Technical Interviews with the Team (90 mins). You’ll meet members of the team, and one of our Co-Founders, to dig into your technical skills around modelling and machine learning engineering
• Culture-Add Interview with Stakeholders (45 min). The final session will be with our CEO and CTO, and will explore how your values align with ours, and is designed to be a genuine two-way conversation, your chance to understand what it’s really like to work at tem