Senior Research Engineer (ML Infrastructure)

Pinely; Amsterdam; On-site; EUR 110,000 - 140,000; Full time

Benefits offered by this job

High base salary

Generous bonus structure

Tuition reimbursement

Job summary

Pinely in Amsterdam is seeking a Senior Research Engineer to build and improve the ML infrastructure powering our deep learning research and trading. You will own the boundary between research and production, turning prototypes into scalable systems and eliminating bottlenecks in the research loop across markets and asset classes.

You will work with researchers and infrastructure teams on training frameworks, datasets, pipelines, distributed training, and observability, with opportunities to

Qualifications

• Strong background in large-scale ML training systems and distributed training.
• ML/data infrastructure, including datasets, storage formats, orchestration, experiment tracking, and configuration.
• Research engineering around large-scale deep learning systems.

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 - make new ideas easier to prototype, experiments faster to run, and failures easier to understand;
• 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;
• When useful, contribute directly to research: run experiments, investigate model behavior, prototype architectural or optimization ideas, and help push model quality forward.

Job description

We are looking for a Senior Research Engineer to build and improve the ML infrastructure that powers our deep learning research and trading.

Our DL strategies are becoming an increasingly important driver of the business, and the infrastructure behind them has a direct impact on how quickly researchers can test ideas, how efficiently we use compute, and how reliably successful research reaches production across markets and asset classes.

You will work at the boundary between deep learning research and infrastructure. Your job is not just to maintain ML systems, but to identify what is slowing research down, solve technically difficult problems across the stack, and turn research prototypes into reliable, scalable production systems.

The scope is broad: training frameworks, datasets and data pipelines, distributed training, experiment infrastructure, reproducibility, observability, and tooling around the research lifecycle. Depending on your strengths, you may go deep in one of these areas or work across several of them.

This is a highly hands-on IC role with substantial technical ownership. You will work closely with researchers, other research engineers, and infrastructure teams, and will be expected to independently drive important problems from diagnosis to production.

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 - make new ideas easier to prototype, experiments faster to run, and failures easier to understand;
• 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;
• When useful, contribute directly to research: run experiments, investigate model behavior, prototype architectural or optimization ideas, and help push model quality forward.

Requirements

• A strong, versatile ML systems / research engineer who is comfortable working on ambiguous problems at the intersection of deep learning and infrastructure;
• Strong background in at least one of:
• large-scale ML training systems and distributed training;
• ML/data infrastructure, including datasets, storage formats, orchestration, experiment tracking, and configuration;
• research engineering around large-scale deep learning systems;
• Strong engineering judgment and a high bar for code quality, reproducibility, observability, and maintainability in a large shared ML codebase;
• Strong ownership and initiative: you notice important problems that do not have a clear owner and are willing to take them from an unclear state to a working solution;
• While prior trading experience can be useful, it is not a prerequisite. Our priority is a first-principles mindset, strong technical depth, and a willingness to rethink how quantitative ML research infrastructure should work.

What we offer

• High base salary and social benefits;
• Generous bonus structure. We are very flexible in discussing salary and conditions of employment;
• Cutting-edge hardware and software in production, as well as high technical expertise across the company, which allows us to implement bold ideas and achieve great results;
• Ownership over initiatives that directly solve business problems;
• Ability to trade on dozens of international exchanges;
• Flexible workflow and working schedule (no formalism, bureaucracy, pressure or micromanagement);
• Tuition reimbursement, conference and training sponsorship.