Applied ML Engineer Engineering London

Solvo.ai Ltd; Greater London; Hybride; GBP 90 000 - 130 000; Plein temps

Résumé du poste

Solvo.ai Ltd is seeking an experienced ML Engineer to own end-to-end forecasting and recommendation models for carrier rate, capacity, and bookings. You will build a defensible confidence layer and continuously improve the system through production feedback.

You will collaborate with deployment engineers to calibrate models against business rules and ensure outputs remain safe and actionable for commercial teams.

Qualifications

• 4+ years building and shipping ML systems in production.
• Strong Python and modern ML stack; capable of end-to-end model lifecycle.
• Bias for measurable business impact over model elegance.
• Clear written and verbal communication.

Responsabilités

• Design, train and ship forecasting and recommendation models against real carrier rate, capacity and booking data.
• Build the confidence and explainability layer so every recommendation carries its rationale and a confidence band.
• Turn the override loop into training signal — every accept, adjust or reject makes the system sharper.
• Partner with deployment engineers to calibrate models against the rules business managers actually use.
• Own model quality in production: monitoring, drift detection, retraining and the guardrails that keep outputs safe.

Connaissances

Python

ML in production

Communication

Model deployment

Time-series forecasting

Description du poste

You will own models that sit at the centre of a carrier's commercial workflow — recommending prices, quantifying confidence and learning from every accept-or-override. This is applied work: the measure of success is revenue moved and recommendations accepted, not papers published.

You'll work end-to-end, from joining rate, capacity, booking and contract data into a single weighted view, through to the calibration and guardrails that make a recommendation defensible enough to send to a customer.

What you'll do

• Design, train and ship forecasting and recommendation models against real carrier rate, capacity and booking data.
• Build the confidence and explainability layer so every recommendation carries its rationale and a confidence band.
• Turn the override loop into training signal — every accept, adjust or reject makes the system sharper.
• Partner with deployment engineers to calibrate models against the rules trade managers actually use.
• Own model quality in production: monitoring, drift detection, retraining and the guardrails that keep outputs safe.

What we're looking for

• 4+ years building and shipping ML systems that ran in production, not just notebooks.
• Strong Python and the modern ML stack; comfortable owning a model from data to deployment.
• A bias for measurable business impact over model elegance.
• Clear written and verbal communication — you can explain a recommendation to a commercial team.

Nice to have

• Experience with time-series forecasting, pricing or demand models.
• Exposure to logistics, shipping, travel or other yield-managed industries.
• Comfort working directly with customers during a deployment.

About Solvo

Solvo.ai builds the decision layer for revenue management in container logistics. Container shipping moves $14 trillion of goods a year, yet the pricing decisions that move it still run on spreadsheets, gut feel and lag. We sell repeatable commercial outcomes — measured in revenue per TEU, not dashboards opened.

We're a global, technical team spread across Cambridge, London and Singapore, backed by Sequoia, Speedinvest and Frontline, and already live with top-tier carriers. You'll work directly with the people who've sat in the trade room and the engineers building the system that learns from them.