DeepFin Research
HFT Quantitative Research and Trading
Skills
1 month ago
Share this opportunity
Share this opportunity to other talents of your network:
✓ Offer them a visibility boost with clients.
✓ Help your contacts find their next job.
Important information
Contract type:
Permanent
Location:
London, UK
Work mode:
Onsite
Published on:
15 September 2026
What they need
DeepFin is a systematic proprietary trading firm combining deep learning, traditional quantitative research methods, and cutting-edge trading technology, to trade global markets. Founded by engineers and researchers, we build and deploy advanced trading systems that operate across global markets.
Our team is lean, highly technical, and impact-driven - every hire plays a direct role in shaping the firm’s technology, strategy, and performance. We value curiosity, precision, and collaboration, and we’re building an environment where exceptional people can do their best work at the intersection of AI and financial markets.
HFT Quantitative Research and Trading
The Role We are looking for exceptional HFT Quantitative Researchers and Traders to work at the intersection of research, engineering, low-latency execution, creating alpha and monetising our deep-learning alpha alpha across a broad spectrum of markets, products, and asset classes.
Key Responsibilities
- Monetise core alpha through efficient execution strategies across futures and related products.
- Expand trading universes - identify and onboard new instruments within existing markets and work closely with BD to identify new opportunities and exchanges.
- Develop and refine execution logic - optimise market access, routing, and order placement to minimise slippage and latency impact.
- Collaborate closely with researchers, developers, and infrastructure engineers to bring models from research to production.
- Design and test short-term signals and execution algorithms to preserve alpha and reduce transaction costs.
- Monitor live strategies, diagnose performance drift, and implement continual improvements.
- Conduct PnL attribution and microstructure analysis to measure and enhance monetisation efficiency.
- Stay ahead of exchange microstructure changes and proactively adapt strategies to evolving market dynamics and market impact dynamics.
Ideal Candidate Profile
- 5+ years of experience in HFT or ultra-low-latency trading, ideally within futures markets (options experience is a strong plus).
- Proven track record in execution alpha monetisation - not just signal generation - you’ve taken models from idea to production.
- Deep understanding of L3 data, market microstructure, order book dynamics, and short-horizon predictive models.
- Hands-on experience in strategy deployment and productionisation, including calibration, risk controls, and post-trade analytics.
- Strong quantitative and programming skills - Python and C++ preferred; familiarity with distributed and low-latency systems advantageous.
- Knowledge of cross-exchange arbitrage, market making, or execution cost optimisation a plus.
- Highly analytical, commercial mindset with the ability to operate autonomously in a fast-paced, research-driven environment
If you’re passionate about applying advanced technology to real-world markets and want to work alongside a focused, high-performing team, we’d love to hear from you. DeepFin offers a collaborative, research-driven environment where ideas move quickly from concept to execution and where every contribution has visible impact.
Join us in building the next generation of deep-learning-driven trading systems - shaping the future of finance through innovation, rigour, and technology.
Other offers great for you!
These companies are also looking for great profiles
Yunik
Tech Lead IA Agentique / RAG – Azure & Python
Freelance
Paris, France
Hybrid
Skills
12 hours ago
Quick apply
Yunik
Data Analyst Senior - Supply Chain
Freelance
Paris, France
Hybrid
Skills
9 hours ago
Quick apply
Groupbees
Pilote de projet Data - Niort
Freelance
79000 Niort, France
Onsite, Hybrid
Skills
7 hours ago
Quick apply
No company information available.