Senior Staff MLE - Dynamic Pricing

tem

📈 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 Rosso is tem's core IP. It's the transaction infrastructure that replaces what a traditional trading desk does — forecasting energy prices and volume, building a real-time picture of the portfolio, optimising the fees placed on every quote, and autonomously managing hedging decisions. All of it running continuously. All of it on the critical path for every deal tem closes. Machine learning is at the heart of it. Rosso combines forecasting, optimisation and classical ML to process billions of data points and drive thousands of automated decisions a day. Every inference shapes the prices our customers see. We've proved the concept. tem now serves 2% of the UK market. The next step is building a pricing engine that doesn't just react — one that proactively drives growth by targeting the right customers at the right time, at the right price, while protecting margin and portfolio balance. Then taking that internationally. We're looking for a Senior Staff Machine Learning Engineer to own pricing ML within Rosso. This is a hands-on senior IC role with real technical authority — you set the strategy, define the mathematical approach, build the models, and ship them. You work closely with MLOps and software engineers, but you don't wait on them. The hard part of this job is the formulation, not the infrastructure. 🚀 Responsibilities * Own the technical direction for pricing ML. Define what to build and how. Set the roadmap for the pricing engine as a core piece of tem's IP — and be accountable for its performance. * Formulate and solve the pricing problem properly. The mathematical foundation doesn't fully exist yet. Your first job is to define it: a dynamic, real-time system that simultaneously optimises for signing probability, portfolio balance, and margin. Choose the right approach — stochastic programming, reinforcement learning, classical ML, or a hybrid — based on the problem, not familiarity. * Build and ship models end-to-end. Own the modelling and data layer. Write production-grade Python. Architect models with deployment in mind and carry them through to production — you can execute without being blocked by engineering dependencies. * Solve imbalance problems. Develop probabilistic models to optimise risk management and short-term balancing decisions in a highly dynamic environment. * Be the voice of pricing ML across the business. Commercial, product, and engineering teams depend on this engine. They need to understand what it's doing and why. You make that happen — clearly, without losing precision. 🎯 Requirements Must-haves: * Deep experience building ML systems for pricing, revenue optimisation, or real-time decision-making — at companies where pricing is the product, not a supporting function. Track record of models that reached production and moved commercial metrics. * Strong foundation in stochastic optimisation and probabilistic modelling. The judgement to formulate ambiguous business problems mathematically before reaching for a tool. * First-principles reasoning across methods. You choose between stochastic programming, reinforcement learning, classical ML, or a simple heuristic based on what the problem demands. * The engineering depth to match your modelling. Production-grade Python, high bar for code quality, and the ability to carry models from formulation to deployment without being blocked. * Senior technical leadership. A track record of setting direction for a significant technical area, influencing cross-functional teams, and translating complex model behaviour into clear terms for commercial, product, and engineering stakeholders — so decisions are understood and acted on. Bonus points: * Experience with real-time pricing at scale — ride-hailing, food delivery, logistics, or similar environments where latency and portfolio effects matter. * Familiarity with energy markets, power trading, or portfolio risk management. * PhD or equivalent research depth in a quantitative discipline — statistics, applied mathematics, operations research, or similar. * Ability to reason about trade-offs between optimisation solvers (Gurobi etc.) and gradient-based methods (PyTorch etc.), and the judgement to know when to reach for each. * Experience with causal inference or reinforcement learning in applied commercial settings. 🗣️ Interview Process Our processes normally take around 2–3 weeks from first call to offer — please let us know about any timeline adjustments you need. 1. First call with our Talent Team (30 Mins). This is to understand your experience, motivations, and discuss the role in more detail. 2. Behaviour Interview with our Rosso GM, hiring manage for this role (45 Mins). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working. 3. Technical Interview with the Team (90 Mins). You’ll meet with potential peers in this session and will discuss technical topics and experiences. 4. Culture-Add Interview with Stakeholders (45 Mins). The final session will be with two cross-functional stakeholders, 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. 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.

tem

📈 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: Rosso is tem's core IP, the transaction infrastructure that prices electricity for thousands of businesses, balances portfolios in real time, and sits on the critical path for every deal tem closes. Machine learning is at the heart of Rosso, combining forecasting, optimisation and classical ML to process billions of data points and drive thousands of automated decisions a day. Every inference shapes the prices our customers see, so you can immediately see the impact of your work. We have proved the concept with MVPs and POCs to grow to 2% of the UK market. Now we want to take it to the next level and build towards a state of the art solution, to fuel our expansion in the UK and take Rosso international. We're looking for a Senior Staff Machine Learning Engineer to lead pricing ML within Rosso, building a platform that proactively drives growth by targeting the right customers to sign at the right time. Your primary focus will be the pricing engine, which sets the fees added to every quote tem serves, carefully balancing growth and margin. You will also contribute to the systems which manage both short and long-term imbalance decisions to determine how tem deals with it’s exposure across its portfolio. This is a hands-on, senior individual contributor role with significant technical leadership and organisation-wide influence. You will work closely with other MLEs, software engineers and MLOps to bring models to production, and carry real ownership of the technical direction and accountable for its performance. The right person is energised by the greenfield environment: comfortable taking on ambiguity and able to make progress before the path is fully defined. They have built pricing systems that worked and have learned from the times it hasn't. They'll bring that hard-won judgment to a system where the foundations are still being laid, and where early decisions compound. Success will be turning our current reactive system into a pricing engine which proactively drives growth by targeting the right customers to sign at the right time. 🚀 Responsibilities: * Own the technical direction for pricing ML: Define what to build and how within the pricing engine, setting the strategy and roadmap for pricing machine learning as a core piece of tem's IP. * Build ML systems for price optimisation: Design and implement models that dynamically set prices, balancing the trade-off between signing probability, portfolio balance and margin maximisation. * Solve imbalance problems: Develop probabilistic models to optimise risk management and short-term balancing decisions in a highly dynamic environment. * Bridge modelling and production: Own the modelling and data layer while working closely with software engineers and MLOps to ensure models are architected for production, contributing to system design decisions that affect performance and reliability. * Communicate pricing decisions clearly: Articulate model behaviour, assumptions, and trade-offs to other technical stakeholders so that pricing decisions are understood across the teams that depend on them. 🎯 Requirements: Must-haves: * Deep experience building ML systems for pricing, revenue optimisation, or decision-making under uncertainty, with a track record of models that went from concept to production and delivered measurable commercial impact. * Strong foundation in stochastic optimisation and probabilistic modelling, with the judgement to formulate ambiguous business problems as the right mathematical approach rather than reaching for familiar tools. * Proven first-principles reasoning: you choose between stochastic programming, classical ML, reinforcement learning, or a simple heuristic based on the problem, not the technique you know best. * The engineering craft to match your modelling depth: production-grade Python, a high bar for code quality and system design, and the ability to work alongside software engineers as a technical peer across the full ML lifecycle. * Senior technical leadership in ML: a track record of setting direction for a significant technical area, influencing cross-functional teams, and translating complex model decisions into clear terms for commercial, product, and engineering stakeholders so they are understood and acted on. Bonus points: * Experience with reinforcement learning or causal inference in applied, commercial settings. * Familiarity with energy markets, power trading, or portfolio management. * PhD or equivalent research depth in a quantitative discipline (statistics, applied mathematics, physics, operations research, or similar). * Ability to reason about the trade-offs between optimisation solvers (Gurobi etc) and gradient-based ML methods (PyTorch etc), and the judgement to know when to reach for each. * Experience working with high data throughput systems in production. 🗣️ Interview Process: Our processes normally take around 2-3 weeks from first call to offer - please let us know about any adjustments to timelines that may be required. 1. First call with our Talent Team (30 Mins). This is to understand your experience, motivations, and discuss the role in more detail. 2. Behaviour Interview with our Rosso GM, hiring manage for this role (45 Mins). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working. 3. Technical Interview with the Team (90 Mins). You’ll meet with potential peers in this session and will discuss technical topics and experiences. 4. Culture-Add Interview with Stakeholders (45 Mins). The final session will be with two cross-functional stakeholders, 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. 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.

tem

📈 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: Rosso is tem's core IP, the transaction infrastructure that prices electricity for thousands of businesses, balances portfolios in real time, and sits on the critical path for every deal tem closes. The machine learning models inside Rosso forecasting, pricing, and optimisation are what make those decisions possible. Every inference shapes the prices our customers see. Today, tem's ML platform has solid foundations: Metaflow for orchestration, AWS Batch for compute, and automated CI/CD pipelines already in place. That's got Rosso to where it is. But as the number of model types grows and Rosso scales, the platform needs the next layer: structured experiment tracking, a model registry, production monitoring, and self-service tooling that lets ML engineers move at pace without being blocked on infrastructure. This role exists to build that layer and define what the platform looks like at scale. You will join the Rosso service alongside a Senior MLOps Engineer in a cross-functional team of ML engineers and software engineers. The destination is a platform that is genuinely self-service: ML engineers can run experiments, compare models, and ship to production without external intervention. It needs to scale across long-horizon forecasting tasks, real-time pricing engines, and large-scale optimisation workloads — not just the models that exist today. The concrete work ahead is specific: experiment tracking and a model registry are not yet in place. Backtesting infrastructure critical to the forecasting mission needs to be built. Shadow deployments will allow new models to be validated in production before they go live. And the platform needs to be designed for heterogeneous workloads, not just the models that exist today. This is a technical leadership role: you'll define the platform strategy and set the direction for the MLOps, while remaining hands-on in the most critical architectural decisions. The right person has seen ML platforms scale well and has learned from the times they haven't. You'll bring that judgment to a platform that can't afford expensive detours. 🚀 Responsibilities: * Own the ML platform strategy: Define the roadmap from Level 1 to Level 2, making architectural decisions ahead of when they'd otherwise become blockers. Keep the platform aligned to Rosso's commercial trajectory. * Build the foundations: Lead the design and build of experiment tracking, model registry, automated pipeline infrastructure, and production monitoring across all model types. * Deliver backtesting and shadow deployments: Build the infrastructure the forecasting and pricing teams need to validate models reliably against historical data and in production before they go live. * Set technical direction: Provide the architectural vision and standards the Senior MLOps Engineer executes against. This is a force-multiplier relationship, not a management one. * Partner across the team: Work closely with ML engineers and software engineers to understand what the platform needs to unlock the next wave of Rosso capabilities. Translate those needs into principled platform decisions. * Choose the right tools: Evaluate the MLOps tooling ecosystem with clear eyes. Make choices that fit tem's scale and workload mix not what's fashionable. * Drive deployment reliability: Push toward more frequent, reliable model deployment cycles as Rosso moves from batch-heavy workflows toward live, near-real-time processes. * Define best practices: Establish standards for how models are trained, versioned, deployed, and monitored across the team. Create a platform ML engineers trust. What success looks like: * MLOps is no longer a bottleneck, ML engineers are unblocked to focus on model quality * The time to deploy new machine learning models goes from days to minutes * The core features required from the machine learning platform are delivered before they block progress e.g. backtesting and experiment tracking 🎯 Requirements: Must-Haves: * Scaled an ML platform from early-stage: Demonstrable experience taking an ML platform from early stages to best-in-class infrastructure at a fast-moving company. You've been there, done it, and you're comfortable with the messiness and ambiguity that comes with scale-up life. * ML pipeline expertise: Deep experience across the whole MLOps lifecycle with ML pipeline orchestration (Metaflow, Prefect, Airflow or equivalent) and ML infrastructure (Sagemaker, Vertex AI, Chalk, or equivalent). * Model lifecycle tooling: Hands-on experience building or operating experiment tracking systems (MLflow, W&B, or similar), model registries, and governance tooling for model fleets at scale. Knows what good looks like and what to avoid. * Broad MLOps tooling knowledge: Across the ecosystem monitoring, drift detection, CI/CD for ML, containerisation, IaC (Terraform, AWS CDK). Able to evaluate trade-offs and make principled choices for a specific context, not just default to what they know. * Technical leadership track record: Evidence of setting platform direction, influencing cross-functional teams, and defining standards at Staff+ level. Raises the quality bar through design reviews, code reviews, and mentoring. Knows when to drive strategy and when to get into the weeds. * Heterogeneous workload experience: Experience designing and operating platforms serving heterogeneous workloads (e.g. forecasting, classification, operations research, etc), not just one model type across batch and real time applications. * Python, AWS + IaC: Strong Python; hands-on experience with AWS and infrastructure-as-code (Terraform, AWS CDK). Bonus points: * Worked in a role where ML is at the core of the product * Familiarity with Metaflow specifically * Experience with operations research, large-scale optimisation in a production context * Experience working with business critical time series forecasting models * Exposure to reinforcement learning in a production setting * Exposure to production LLM workloads e.g. fine tuning 🗣️ Interview Process: Our processes normally take around 2-3 weeks from first call to offer - please let us know about any adjustments to timelines that may be required. 1. First call with our Talent Team (30 mins). This is to understand your experience, motivations, and discuss the role in more detail. 2. Behaviour Interview with Tim, Head of Data (60 mins). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working. 3. Technical Interview with the Team (90 mins). You'll meet with potential peers in this session and work through a live technical exercise. 4. Culture-Add Interview with Stakeholders (45 mins). The final session will be with two cross-functional stakeholders, 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. 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.