Platinum & Partners is retained by a top-tier systematic investment firm to identify an exceptional Deep Learning Quantitative Researcher. This is one of the most demanding and specific candidate profiles in quantitative finance — the firm is seeking someone with a rare combination of elite academic pedigree, deep learning expertise at scale and the research discipline to operate rigorously in a low signal-to-noise domain.
The successful candidate will own a significant part of the firm's deep learning research agenda — designing and building core pipelines for applied quantitative alpha research, acting as the central deep learning authority, and driving the full empirical loop from problem formulation through to production deployment.
THE ROLE
- Design and build the firm's core deep learning pipelines for applied quantitative alpha research — from data preparation and distributed training through to evaluation and production deployment
- Drive a significant part of the research agenda using applied deep learning: problem formulation, model design, training, validation and performance attribution — with full empirical ownership
- Uphold rigorous research discipline in a low signal-to-noise domain: strict out-of-sample hygiene, leakage prevention and honest benchmarking against simpler baselines
- Act as the firm's central point of deep learning expertise — advising on architecture selection, training diagnostics, model design reviews and standards for model evaluation and promotion
- Facilitate the seamless flow of model fitting and computation across teams and systems through standardised training and inference interfaces and reusable components
WHAT THEY ARE LOOKING FOR
- Top-tier academic background from a globally top-20 university — MIT, Harvard, Princeton, Stanford, Caltech or equivalent
- PhD in Computer Science, Engineering, Physics, Mathematics or Statistics — strongly preferred
- Gold medal in a national or international olympiad (IMO, CMO, IOI, NOI, IPhO, CPhO) — strongly preferred; this is not a box-ticking exercise, it signals the calibre of intellectual ability the firm is seeking
- 3–5 years of professional experience applying deep learning to large-scale problems, ideally in quantitative finance; a strong PhD research record plus hands-on experience training large models at a leading AI/technology company will be considered
- Proven end-to-end ownership of the deep learning model lifecycle on at least one significant production system or published research line
- Deep Python expertise and command of a modern deep learning framework
- Hands-on experience with large-scale model training: distributed/multi-GPU, mixed precision, throughput profiling and optimisation
- Strong foundations in statistics, optimisation and machine learning theory
- Command of modern DL architectures — and the judgement to know when a simpler model should win
- Experience with large-scale datasets: efficient columnar formats, streaming data loaders and point-in-time-correct dataset construction
- Fluency with experiment management tooling: experiment tracking, hyperparameter optimisation and reproducible research environments
- C++ or CUDA-level optimisation experience is a plus; familiarity with LLM tooling as a research accelerant is a plus
The profile — beyond technical skills:
- Research taste and rigour: designs clean experiments and kills ideas quickly when the evidence says so
- Proactive collaborator: builds strong partnerships across research and engineering
- Superb communicator: explains model behaviour and uncertainty to both technical and non-technical audiences
- Growth mindset: stays current with a fast-moving field and adopts what works
WHY THIS ROLE
- A top-tier systematic investment firm with the data, compute resources and intellectual ambition to pursue the hardest problems in applied deep learning for finance
- Central, foundational research mandate — you are setting the standards and building the infrastructure others will depend on
- Rare environment where olympiad-level mathematical ability and production-grade deep learning engineering are equally valued
- Significant autonomy over a major part of the firm's research agenda — this is not a support role
- Exceptional total compensation reflecting the extraordinary rarity of this profile