Quantitative Developer – Agentic Research Lab

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.

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 config hash, data snapshot and code version, so nothing an agent produces is unreproducible

Build continuous monitoring with agentic triage

• Turn sim-vs-live comparisons, PnL, inventory, exposure, latency and regime indicators into alerts
• Have agents triage alerts first: gather context, check known causes, and draft an incident summary with a recommended action for the trader on call

Run controlled experiments in production

• Own the path from shadow mode to paper trading to small live allocations, with explicit promotion criteria, kill-switches and rollback drills at every stage
• Design the experimentation framework — A/B and interleaved parameter tests across venues and instruments — and the statistics that decide them

Guard against automated overfitting

• Build defences into the loop: a budget of trials per hypothesis, multiple-testing corrections (deflated Sharpe, false-discovery control), untouched holdouts agents cannot read, and independent re-execution of every claimed result

Evaluate the agents themselves

• Build eval suites with known-answer research tasks (planted signals, known bugs, look-ahead traps) and track how often agents find them, miss them or fake them
• Route cheap models to extraction and classification, reserve frontier models for reasoning, and track cost per research round

Run models on our own hardware

• Stand up secure, on-prem GPU inference for open-weight models so proprietary data and strategy code never leave our infrastructure
• Define which tasks may use external APIs and which must stay on-prem

Ways of working

• Ship through code review and CI like the rest of engineering — no exceptions for agent-authored code or prompts
• Document architecture decisions, promotion criteria and kill-switch conditions so another engineer can operate the loop without you
• Own your systems end to end, from design through production monitoring and incident response
• Research rounds completed per week without human authoring, and the share that end in a reviewed verdict
• Share of agent-reported results that reproduce on independent re-execution
• Time from a live-performance anomaly to a triaged summary in front of a trader
• Strategies promoted through the shadow → live path, and incidents caused by that path (target: zero)

• 10+ years building production software or ML systems, including several years as a technical lead who set architecture for other engineers
• Agents in production, not demos: multi-agent orchestration, tool calling, evaluation and red-teaming, cost and model routing, staged rollouts with a human in the loop
• Hands-on quantitative experience: you have built backtests, factor or signal models, or risk models, and know how they lie — look-ahead bias, data snooping, regime change and transaction costs are second nature
• MLOps and CI/CD at platform level: you have built (not just used) model registries, experiment tracking, automated validation and deployment pipelines, and treat reproducibility as a hard requirement
• Streaming and analytical data systems in production: Kafka (or Redpanda) and ClickHouse, including schema design, partitioning, consumer lag and query performance at high volume
• Strong Python and SQL
• At ease on Linux: you debug on the box (processes, memory, networking, I/O, perf/strace-level tooling) and run workloads on Kubernetes with infrastructure-as-code
• Experience shipping automated decision systems in regulated or capital-at-risk environments: kill-switches, audit trails, compliance review
• Judgement to know which parts of research to automate, and which must stay with a human
• Production experience in a systems language — Rust, C++ or Go — with Rust especially valuable, since the trading engine and simulator are written in it
• Experience in a trading environment, ideally market making, HFT or crypto (perps, funding, on-chain venues)
• Market microstructure knowledge: order books, queue position, markouts, adverse selection
• MCP server design, or open-weight model serving (vLLM, SGLang or similar) on your own GPUs
• Experience with Delta Lake, Dagster, MLflow, GitOps (Flux / Argo CD) or Terraform

• International environment (English is the main language)
• Team events and offsites
• Learning and Development budget
• Health insurance for employee and legal dependents

Recruitment Process

Our process is consistent across all roles and is designed to assess genuine technical depth. Candidates should expect the following stages:

• Stage 1 - Initial screening with TA (30 mins)
• Stage 2 - Technical Screen - Meet the Data Director (45 mins)
• Stage 3 - Quant Interview - Meet the Quant Developers and Quant Researchers teams (60 mins)
• Stage 4 - Roundup Interviews - Meet the Infrastructure Director, the Data team, and the Chief Technology Officer (90 mins)

On the agenda, discussions rather than trick questions! These moments of exchange will allow you to understand how Flowdesk works and its values. But they are also (and above all) an opportunity for you to present your career path and your expectations for your next job!

We are committed to an inclusive and accessible recruitment process. If you require any reasonable adjustments or have specific needs to enable you to participate fully in the interview or assessment process (e.g., a sign language interpreter, extra time for a test, or an accessible location), please contact us to discuss how we can support you.