Senior Staff Machine Learning Engineer – ML Platform

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. Build systems that deploy, iterate and scale ML and optimisation models .

Posted 11/9/2026full-timeRemote • United KingdomSenior💰 £115,000 - £127,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying machine learning models in production, with a strong focus on MLOps practices, data system design, and operational monitoring. Proficient in scaling ML workloads and ensuring reliability and observability across complex systems.

Highest-signal resume keywords

ML Model DevelopmentMLOps Stack BuildingData Pipeline DesignTime-Series ForecastingOperational Monitoring

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning EngineeringModel DeploymentData Systems DesignProbabilistic ModellingBacktesting FrameworksQuantitative AnalysisModel InferenceModel TrainingData ValidationDatabase Management

Soft Skills

Fluent English Communication

Industry Keywords

FinanceEnergyAutomated Trading SystemsModel Performance MonitoringData Contracts

About the role

Key responsibilities & impact

Build systems that deploy, iterate and scale ML and optimisation models
Improve ML platform infrastructure, tooling and standards
Provide operational visibility into model performance
Build evaluation and backtesting tooling for ML models, pricing and trading strategies
Own model inference and training in production
Participate in work-hours on-call responsibility, with possible out-of-hours on-call in the future
Shape infrastructure for ML deployment across pricing, forecasting, prediction and optimisation
Design repeatable systems to move models from experiment to production
Scale models and pipelines as volume, markets and complexity grow
Build reliability, operational monitoring and analytical monitoring for models
Detect failures, drift and degradation early
Validate input data and establish clear data contracts
Mature databases, pipelines, validations and ingestions for safe model rollouts
Contribute directly to model improvements where ML expertise has impact

Requirements

What you’ll need

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 scaling pressures and complexities of large-data ML workloads
Experience designing data systems, including pipelines, databases and query patterns
Experience with time-series forecasting
Knowledge of what good looks like in an ML platform
Probabilistic modelling and quantifying uncertainty, such as probabilistic forecasts, calibration or Bayesian methods (bonus)
Quant tooling experience, such as building backtesting frameworks or evaluation tooling for trading or optimisation strategies (bonus)
Experience in finance or energy, including automated trading systems (bonus)
Fluent English speaking, reading and writing required
Ability to work from the stated country of residence with appropriate work authorization or sponsorship

Benefits

Comp & perks

Stock options so everyone has ownership in our mission
25 days holiday plus public holidays
Ability to swap public holidays for preferred alternatives
Birthday off
Remote first and flexible working
Clear core hours
No internal meetings on Friday afternoons
Up to GBP1,200 or EUR1,200 per year for remote setup
Up to GBP150 or EUR150 per month for wellbeing