Machine Learning Engineering Manager

The Role:

We're looking for an Machine Learning Engineering Leader 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.

• 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 focused on what matters.

• Enable without adding overhead: You are a sounding board, not a technical authority. Ask the right questions, help surface risks, and create space for experts to make good decisions—without positioning yourself as another review layer.

• Drive collaboration with the Rosso Engineering Manager: Partner closely to align priorities between ML and software engineering. The two teams need to work together effectively, and you are a key part of making that happen.

Must haves:

• Ownership orientation: You want accountability for outcomes, not just oversight of a team. You’re comfortable holding the pen on strategy, budget, and people—and being the person the GM holds to account when the numbers aren’t moving.

• Strong management experience: Proven experience managing ML engineers or scientists at varying ranges of experience (Junior to Staff), with enough understanding of the ML lifecycle and core disciplines including forecasting, optimisation, pricing, and classical ML to manage credibly.

• A strong people development track record: 1:1s and performance conversations that actually move people forward, action underperformance, clear progression frameworks, and coaching that builds capability across engineers at different career stages.

• Experience building and owning team operating systems: the prioritisation frameworks, sprint cadences, incident review processes, and feedback loops that make a technically complex team perform consistently.

• A strong hiring instinct for ML roles: you have defined the bar, built pipelines in a competitive market, and brought in strong people who had other options.

• Experience managing a technically diverse team: comfortable holding substantive conversations across different ML problem types and helping a multidisciplinary team prioritise and operate without being the expert in any single domain.

• Experience in a startup…