Research Platform Engineer
⚲ Amsterdam (relocation possible)/Remote
About the Role
As a Research Platform Engineer, you will design and build the platform that connects quantitative research with production trading.
You will build the infrastructure, environments, and interfaces that allow researchers to move from hypothesis to production without rewriting their work. The core constraint we are solving is not nanoseconds — it is research throughput: how quickly and reliably we can generate, test, evaluate, and deploy new ideas.
The platform will be designed from day one for an increasingly autonomous research process, where AI agents can access data, run experiments, evaluate results, and promote or discard ideas through programmatic and measurable interfaces.
You will work on
Design and build the research platform from scratch, defining its architecture and core abstractions
Build a seamless path from research to production, with reproducible environments and deployment workflows
Define contracts between research and production: APIs, artifact formats, data contracts, and promotion gates
Build a unified data layer that makes new market and alternative data sources easy to integrate and consume
Develop backtesting and simulation infrastructure, including methods to measure deviation between backtested and live results
Design platform interfaces for researchers and AI agents: programmatic, deterministic, self-describing, and machine-readable
Work directly with researchers to identify bottlenecks and turn them into platform capabilities
• Make pragmatic build-vs-buy decisions and optimize the platform for research velocity, reliability, and adoption
About the Team
We are building a research lab first — and a proprietary investment fund around it. The core belief is that long-term trading edge comes from a systematic ability to generate, test, and compound research insights faster and more rigorously than the market.
The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI. The goal is to tightly integrate execution, research, and infrastructure into a single coherent system.
You might thrive in this role if you have
Experience building developer platforms, internal tooling, research infrastructure, or systems other engineers build on
Strong systems programming skills in Go and/or Rust, with a deep understanding of concurrency, memory, runtimes, and performance
Experience designing complex systems from scratch with clear abstractions and interfaces
Experience with data and distributed systems; Kafka, Postgres, or ClickHouse is a plus
Experience working directly with internal users and translating real workflow bottlenecks into technical solutions
Strong judgment around correctness, reproducibility, and build-vs-buy decisions
Ability to design systems for programmatic consumers with deterministic behavior and machine-readable outputs
Experience with agentic systems, quantitative research, or financial markets is a plus
Research Platform Engineer
⚲ Amsterdam (relocation possible)/Remote
About the Role
As a Research Platform Engineer, you will design and build the platform that connects quantitative research with production trading.
You will build the infrastructure, environments, and interfaces that allow researchers to move from hypothesis to production without rewriting their work. The core constraint we are solving is not nanoseconds — it is research throughput: how quickly and reliably we can generate, test, evaluate, and deploy new ideas.
The platform will be designed from day one for an increasingly autonomous research process, where AI agents can access data, run experiments, evaluate results, and promote or discard ideas through programmatic and measurable interfaces.
You will work on
Design and build the research platform from scratch, defining its architecture and core abstractions
Build a seamless path from research to production, with reproducible environments and deployment workflows
Define contracts between research and production: APIs, artifact formats, data contracts, and promotion gates
Build a unified data layer that makes new market and alternative data sources easy to integrate and consume
Develop backtesting and simulation infrastructure, including methods to measure deviation between backtested and live results
Design platform interfaces for researchers and AI agents: programmatic, deterministic, self-describing, and machine-readable
Work directly with researchers to identify bottlenecks and turn them into platform capabilities
• Make pragmatic build-vs-buy decisions and optimize the platform for research velocity, reliability, and adoption
About the Team
We are building a research lab first — and a proprietary investment fund around it. The core belief is that long-term trading edge comes from a systematic ability to generate, test, and compound research insights faster and more rigorously than the market.
The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI. The goal is to tightly integrate execution, research, and infrastructure into a single coherent system.
You might thrive in this role if you have
Experience building developer platforms, internal tooling, research infrastructure, or systems other engineers build on
Strong systems programming skills in Go and/or Rust, with a deep understanding of concurrency, memory, runtimes, and performance
Experience designing complex systems from scratch with clear abstractions and interfaces
Experience with data and distributed systems; Kafka, Postgres, or ClickHouse is a plus
Experience working directly with internal users and translating real workflow bottlenecks into technical solutions
Strong judgment around correctness, reproducibility, and build-vs-buy decisions
Ability to design systems for programmatic consumers with deterministic behavior and machine-readable outputs
Experience with agentic systems, quantitative research, or financial markets is a plus
Research Platform Engineer
⚲ Amsterdam (relocation possible)/Remote
About the Role
As a Research Platform Engineer, you will design and build the platform that connects quantitative research with production trading.
You will build the infrastructure, environments, and interfaces that allow researchers to move from hypothesis to production without rewriting their work. The core constraint we are solving is not nanoseconds — it is research throughput: how quickly and reliably we can generate, test, evaluate, and deploy new ideas.
The platform will be designed from day one for an increasingly autonomous research process, where AI agents can access data, run experiments, evaluate results, and promote or discard ideas through programmatic and measurable interfaces.
You will work on
Design and build the research platform from scratch, defining its architecture and core abstractions
Build a seamless path from research to production, with reproducible environments and deployment workflows
Define contracts between research and production: APIs, artifact formats, data contracts, and promotion gates
Build a unified data layer that makes new market and alternative data sources easy to integrate and consume
Develop backtesting and simulation infrastructure, including methods to measure deviation between backtested and live results
Design platform interfaces for researchers and AI agents: programmatic, deterministic, self-describing, and machine-readable
Work directly with researchers to identify bottlenecks and turn them into platform capabilities
• Make pragmatic build-vs-buy decisions and optimize the platform for research velocity, reliability, and adoption
About the Team
We are building a research lab first — and a proprietary investment fund around it. The core belief is that long-term trading edge comes from a systematic ability to generate, test, and compound research insights faster and more rigorously than the market.
The goal is to remove legacy constraints and rethink financial market forecasting using modern systems, new hardware paradigms, and frontier AI. The goal is to tightly integrate execution, research, and infrastructure into a single coherent system.
You might thrive in this role if you have
Experience building developer platforms, internal tooling, research infrastructure, or systems other engineers build on
Strong systems programming skills in Go and/or Rust, with a deep understanding of concurrency, memory, runtimes, and performance
Experience designing complex systems from scratch with clear abstractions and interfaces
Experience with data and distributed systems; Kafka, Postgres, or ClickHouse is a plus
Experience working directly with internal users and translating real workflow bottlenecks into technical solutions
Strong judgment around correctness, reproducibility, and build-vs-buy decisions
Ability to design systems for programmatic consumers with deterministic behavior and machine-readable outputs
Experience with agentic systems, quantitative research, or financial markets is a plus
