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Recruitment

Join Tomtit
Capital.

We hire for depth of thinking, intellectual honesty, and the ability to build things that work in production β€” not just in theory. If that describes you, read on.

Modern research office
01 β€” Departments

How we're
organised.

Research
Quantitative Research
The engine of the firm. Researchers design, backtest, and validate systematic strategies. Every hypothesis is tested out-of-sample. Every model has an economic mechanism.
Engineering
ML Engineering
We build our own infrastructure β€” data pipelines, model training, execution systems, monitoring. Engineers here own production end-to-end, from ingestion to live P&L.
Risk
Risk Management
Risk is not a constraint on our strategy β€” it is the strategy. This team owns position limits, drawdown controls, correlation monitoring, and kill-switch protocols.
Operations
Operations & Compliance
Everything that keeps the firm running cleanly β€” counterparty relationships, regulatory compliance, financial reporting, and the operational infrastructure behind the research.
02 β€” Programs & Events

How we engage
with the world.

Research Symposium
An annual internal conference where all teams present their research findings, strategy retrospectives, and forward-looking hypotheses. Open to intern cohort members.
Annual Β· Internal
Quantitative Reading Group
A bi-weekly paper review session open to all team members. We work through recent academic research in ML, market microstructure, and quantitative finance.
Bi-weekly Β· All staff
Model Review Week
A quarterly intensive where all live strategies are audited for signal decay, overfitting risk, and regime sensitivity. Every model earns its continued operation.
Quarterly Β· Research
Intern Research Sprint
The capstone of our internship programme β€” interns present original research findings to the full team. The best ideas are developed into live research projects.
Annual Β· Interns
03 β€” Open Roles

Current
openings.

Quantitative Researcher
ResearchΒ·  Full-timeΒ·  Remote
+

Design, backtest, and deploy systematic trading strategies. You think in hypotheses, demand out-of-sample evidence, and are honest enough to retire something when its edge decays.

  • Strong foundations in probability, statistics, and linear algebra β€” not just the libraries
  • Experience building quantitative models with rigorous out-of-sample validation
  • Fluent Python; PyTorch experience valued
  • Understanding of market microstructure and transaction cost analysis
  • Intellectual honesty: you report what the data says
ML Engineer β€” Trading Systems
EngineeringΒ·  Full-timeΒ·  Remote
+

Own the infrastructure from data ingestion to live production β€” pipelines, model serving, execution systems, and monitoring. You write code that does not fail silently at 3am.

  • Production Python in data-intensive environments; type hints and tests are defaults
  • Time-series databases and pipeline orchestration (Prefect, Airflow)
  • ML deployment and drift monitoring
  • Docker, cloud infrastructure, CI/CD
04 β€” Internship

Research placement
for exceptional students.

Our internship is an intensive research placement for students in mathematics, statistics, computer science, or physics. You will work on real problems β€” not curated exercises β€” and be treated as a collaborator, not a guest.

The placement runs for 10–12 weeks. At the end, interns present their research findings to the full team. The best ideas become live research projects.

05 β€” Our Interview Process

What to expect
from us.

1
Application Review
Mark Synytsia reads every application personally. We look for specific thinking, not credentials. We respond to every application within 7 days.
2
Research Problem
A take-home research problem relevant to the role. We are assessing how you think and how you communicate β€” not how quickly you can answer.
3
Conversation
A 60-minute conversation about your work, your thinking, and how you'd approach the problems we're working on. No trick questions. No performative puzzles.