2 days ago

Senior Research Engineer (ML infrastructure)

pinely

Amsterdam

2 days ago

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We are looking for a Senior Research Engineer to build and improve the ML infrastructure that powers our deep learning research and trading. The role involves working at the boundary between deep learning research and infrastructure, turning research prototypes into reliable, scalable production systems. The ideal candidate has a strong background in ML systems and infrastructure, with hands-on experience making ML research faster or more reliable.

Core AIHybridFull-timeSeniorMachine LearningDeep Learning

Senior Research Engineer (ML infrastructure)

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Core AIHybridFull-timeSeniorMachine Learning

Salary

Not specified

Work Location

Amsterdam, North Holland, Netherlands, NL

Work Model

Hybrid

Employment Type

Full-time

Experience Level

Senior

Core Qualifications

Technical (Must-have)

Machine LearningDeep LearningML infrastructureDistributed trainingData pipelinesExperiment trackingReproducibilityObservabilityGPU/compute infrastructureOrchestrationCode reviewPython

Soft Skills

OwnershipInitiativeCollaborationProblem solvingCommunicationEngineering judgment

Key Responsibilities

•Turn successful research prototypes into robust implementations that can be trained, validated, and deployed across multiple markets and asset classes.
•Work directly with researchers to remove infrastructure bottlenecks from the research loop.
•Build and improve our internal ML platform — training framework, datasets and data pipelines, orchestration, experiment tracking and reproducibility tooling, GPU/compute infrastructure, and tooling for releasing models into production.
•Own technically challenging areas of the ML stack end-to-end: identify problems, design solutions, implement them, measure their impact, and maintain them in production.
•Make training reproducible and observable: investigate regressions, debug models that no longer reproduce from master, and improve tooling around data, experiments, model quality, and training behavior.
•Take ownership of shared training, dataset, and research infrastructure code — proactively find bugs, reduce technical debt, improve abstractions, and maintain a high bar through rigorous code review.
•Work across team boundaries when a research problem spans datasets, storage, compute, training infrastructure, or production systems.
•Identify areas where researchers are repeatedly paying an infrastructure tax and build reusable solutions instead of fixing the same problem case by case.
•When useful, contribute directly to research: run experiments, investigate model behavior, prototype architectural or optimization ideas, and help push model quality forward.

Senior Research EngineerML infrastructureDeep LearningTradingAmsterdamHybridFull-timeMachine LearningResearch Engineering

Key Responsibilities

•Turn successful research prototypes into robust implementations that can be trained, validated, and deployed across multiple markets and asset classes.

•Work directly with researchers to remove infrastructure bottlenecks from the research loop.

•Build and improve our internal ML platform — training framework, datasets and data pipelines, orchestration, experiment tracking and reproducibility tooling, GPU/compute infrastructure, and tooling for releasing models into production.

•Own technically challenging areas of the ML stack end-to-end: identify problems, design solutions, implement them, measure their impact, and maintain them in production.

•Make training reproducible and observable: investigate regressions, debug models that no longer reproduce from master, and improve tooling around data, experiments, model quality, and training behavior.

•Take ownership of shared training, dataset, and research infrastructure code — proactively find bugs, reduce technical debt, improve abstractions, and maintain a high bar through rigorous code review.

•Work across team boundaries when a research problem spans datasets, storage, compute, training infrastructure, or production systems.

•Identify areas where researchers are repeatedly paying an infrastructure tax and build reusable solutions instead of fixing the same problem case by case.

•When useful, contribute directly to research: run experiments, investigate model behavior, prototype architectural or optimization ideas, and help push model quality forward.