High-Impact ML Engineer – Elite Quant Trading Firm – New York
My client, one of the world’s most successful quantitative trading, is hiring a Senior Machine Learning Engineer to work at the core of its global trading business. This is a role for PhD‑level engineers who want to operate at the technical bar of the very best research labs and big‑tech teams, but with direct, visible impact on multi‑billion‑dollar trading outcomes.
You’ll join a lean, highly technical engineering group that designs and builds in‑house systems for pricing, execution, and risk across hundreds of venues in ~45 countries, routinely handling trillions of dollars in notional volume each month. Engineers sit next to traders and researchers, ship changes fast, and see their work hit production in days rather than quarters, with tight feedback loops from live markets instead of abstract KPIs.
Key Responsibilities
- Design, build, and productionize ML‑driven systems that power pricing, execution, and risk management for a broad range of liquid asset classes.
- Develop high‑throughput data and feature pipelines over real‑time market data, ensuring robustness, observability, and fast iteration for research and trading.
- Own end‑to‑end model lifecycles: experimental design, training and evaluation, deployment into low‑latency environments, and ongoing monitoring and retraining.
- Collaborate directly with traders, quant researchers, and systems engineers on hard technical problems – from feature construction and simulation frameworks to performance‑critical online inference.
- Optimise performance across the stack (data formats, storage, distributed training, model serving, scheduling) within a very large, well‑tooled monorepo environment.
- Mentor other engineers, help shape technical direction, and “see a problem, fix a problem” across the codebase and tooling ecosystem.
Required Background
- PhD in Computer Science, Electrical/Computer Engineering, Applied Mathematics, Statistics, or a closely related quantitative field.
- Exceptional programming ability in at least one of: Python, C++, Rust, OCaml, or Java, plus experience working in large, collaborative codebases.
- Deep understanding of modern ML (representation learning, time‑series/sequence models, optimisation, rigorous evaluation) and a track record of taking ideas from research to production.
- Hands‑on experience with modern ML frameworks (e.g. PyTorch, TensorFlow, JAX) and supporting infrastructure such as distributed training, experiment tracking, and high‑volume data pipelines.
- Strong systems instincts: you think systematically about latency, throughput, reliability, and debuggability, not just model accuracy.
- Comfortable operating in small, exceptionally strong teams where peers are highly technical and standards are correspondingly high.
- Curiosity about markets and complex systems; prior finance experience is a plus but not a prerequisite.
Why Join
- Thrive in a prestigious, tech‑first hedge fund with one of the most competitive compensation structures in the industry, with total compensation comfortably at the top end of both trading and big‑tech markets.
- Work in a flat, apprenticeship‑style environment where almost all software is written in‑house, so you can go as deep as you want from research tooling down to hardware‑adjacent systems.
- Partner day‑to‑day with front‑office teams on problems that are mathematically interesting, systems‑heavy, and tightly coupled to real P&L.
- Enjoy genuine ownership and autonomy: small teams, low politics, and strong cross‑team support to fix problems wherever you find them.
If you are a PhD‑level ML engineer who enjoys hard technical problems, wants to work alongside some of the strongest engineers in the market, and cares about building rigorous systems that matter every single trading day, I’d be keen to speak and share more detail.