Senior Staff Machine Learning Engineer - ML Platform

About tem

We're rebuilding how energy is bought and sold by cutting out middlemen, connecting businesses directly with renewable generators, and ensuring every transaction is traceable and transparent.

We're a 160+ person team—operators, data scientists, engineers, customer champions, and energy experts—backed by top-tier investors including Lightspeed Venture Partners, Hitachi Ventures, Allianz, AlbionVC, and Atomico. We closed a $75M Series B round in early 2026, taking our total funding to $94M, and we now power energy for over 4,000 UK businesses across 8,500+ sites, with over £75m in value returned to businesses and generators so far.

We're a technology company solving the energy industry. Some call us a neo-utility—the neo-bank idea applied to energy.

The Role

We're hiring a Senior Staff Machine Learning Engineer (ML Platform).

In this role, you will work on the ML platform inside Rosso, improving the infrastructure, tooling, and standards that let us develop, train, evaluate, monitor, and iterate models quickly and with confidence. You know ML engineering deeply and will contribute to model improvements where it matters, with a primary focus on building the platform and tools that help the whole team work faster and better.

What You Will Do

• Build the systems that let us deploy, iterate, and scale ML and optimisation models without friction.
• Give us clear operational visibility into how every model is performing, and where it is not.
• Build evaluation and backtesting tooling for ML models, pricing, and trading strategies.
• Take ownership of model inference and training in production. The role includes work-hours on-call responsibility, and may extend to out-of-hours in the future.

Key Metric: Number of effective forecasting & optimisation ML models deployed within our stack. Success means a new model goes from idea to trusted production with standard tooling, clear evaluation, and confidence from day one.

Responsibilities

ML Infrastructure and Deployment

• Shape and improve the infrastructure that underpins ML deployment across pricing, forecasting, prediction, and optimisation.
• Design and build the systems that let us take a model from experiment to production quickly and repeatably.
• Scale models and pipelines as volume, markets, and complexity grow.

Observability and Quality

• Build the tooling and code that ensure models run reliably and produce sensible results.
• Create operational and analytical monitoring so we catch failures, drift, and degradation early.
• Build confidence in our input data through validation, checks, and clear data contracts.

Evaluation, Experimentation, and Backtesting Tooling

• Develop the tooling and abstractions to evaluate ML models and optimisation algorithms during experimentation, including backtesting of pricing and trading strategies.
• Standardise processes and data structures so each new model is faster to ship than the last.

Data Systems

• Mature the data systems (databases, pipelines, validations, ingestions) that let us manage model rollouts safely.

Ownership

• Own model inference and training in production, including on-call during working hours.
• Contribute directly to model improvements where your ML expertise moves the needle.

Requirements

Must Haves

• Strong ML engineering foundations: experience developing, deploying, and improving ML models in production.
• Experience contributing to or building an MLOps stack, including ensuring reliability and observability.
• Experience with the scaling pressures and complexities of large-data ML workloads, and designing data systems (pipelines, databases, query patterns) to handle them.
• Experience with time-series forecasting in particular.
• Knowing what good looks like in an ML platform, and being ready to build it in a high-ownership environment.

Bonus Points

• Probabilistic modelling and quantifying uncertainty (e.g., probabilistic forecasts, calibration, or Bayesian methods).
• Quant tooling experience, such as building backtesting frameworks or evaluation tooling for trading or optimisation strategies.
• Experience in finance or energy, including automated trading systems.

Benefits & Perks

• Salary aligned to internal benchmarks and reviewed twice a year.
• Stock options so everyone has ownership in our mission.
• 25 days holiday plus public holidays (swap public holidays for ones that matter most to you) + birthday off.
• Remote-first and flexible working, with clear core hours and no internal meetings on Friday afternoons.
• Home working budget: Up to £1,200 / €1,200 per year for your remote setup.
• Wellbeing budget: Up to £150 / €150 per month.

Interview Process

• Intro call with Talent (30 minutes)
• Behavioural interview (30 minutes)
• Skills interview (90 minutes)
• Bar Raiser interview with leadership stakeholders (30 minutes)