Quantitative Developer – Agentic Research Lab

About Flowdesk At Flowdesk, we are building the infrastructure powering the next era of global finance. As a full-service digital asset trading and technology firm, we provide liquidity solutions, OTC trading, and market infrastructure that connect digital assets to global markets with institutional-grade precision, transparency, and scale. Flowdesk combines expertise from traditional finance, algorithmic trading, and blockchain technology to deliver always-on trading capabilities across the digital asset ecosystem. Founded in 2020, we have grown into a global team supporting institutions, exchanges, and token issuers worldwide as the financial system evolves toward tokenized markets. Why join Flowdesk? You’ll be joining a company operating at the intersection of finance, technology, and crypto - solving complex market problems, building critical infrastructure, and helping shape how digital assets move globally. We move fast, think long term, and work with a strong ownership mindset across engineering, trading, product, operations, and business teams. Quant Development is a new team inside Technology, owning the research platform that lets our quant researchers test thousands of hypotheses a day: the market-data store, the distributed compute systems, and the orchestration and tooling around them. Tick-level order-book data and our own execution traces stream through Redpanda into Delta Lake and ClickHouse, with a Kubernetes cluster running simulation campaigns on top. Mission You’ll be the technical lead for the agentic research lab — setting the architecture, shipping the first production agents yourself, and defining the guardrails that keep an automated research loop honest. Agents formulate hypotheses, write and run experiments, check their own results, and watch live strategies for decay — humans review verdicts, not boilerplate. You’ll work closely with quant researchers, traders and the risk team, as well as the Systems and Research-tooling developers whose platform your agents run on. Tasks Make the research platform production-grade (MLOps & CI/CD) • Treat research like software: version, build, test and deploy every strategy, signal, dataset snapshot and agent prompt through CI/CD (GitHub Actions, GitOps with Flux / Argo CD) • Own the model lifecycle end to end — experiment tracking, model registry, reproducible builds, automated validation gates, staged deployment and rollback — so a model that reaches capital always links back to its code, data and verdict • Put the agents on the same rails: ship their tools, prompts and evals through CI, with a regression in an eval suite failing the PR Plug agents into the live data streams • Consume market data and execution traces from Kafka / Redpanda topics and query them at scale in ClickHouse, so monitoring and research agents see the same fresh data the desks see, within seconds rather than T+1 • Design the topics, schemas and materialized views the monitoring loop needs, with backpressure, replay and exactly-once semantics where they matter Build the research agent loop • Design a multi-agent workflow: a hypothesis generator, an experiment writer that turns a hypothesis into a campaign spec, an executor that runs it on the simulation cluster, and a critic that tries to break the result before a human sees it • Expose the platform to agents as typed tools — an MCP server over the research SDK, the campaign engine and the experiment registry — so agents call the same validated code paths researchers do Make “continuous” real • Run scheduled research rounds (Dagster) on fresh data: re-validate live strategies, re-run parked ideas when the simulator improves, and propose parameter updates • Land every round in the experiment registry (MLflow-style) with its