Machine Learning Engineer – Deep Learning for HFT

About Our Client

Our client is an established quantitative trading firm seeking an AI / Machine Learning Engineer to develop and deploy deep learning models for high-frequency trading and market making.

Reporting to the Head of HFT, you will work on large neural networks trained on raw tick-level order-book data across multiple markets and venues. The role offers ownership across data preparation, model research, training infrastructure and integration into a live C++ trading platform.

You will join a small team with minimal hierarchy, direct access to decision-makers and meaningful ownership of your work. The firm offers top-of-market compensation with P&L-linked upside .

We welcome experienced researchers and engineers from quantitative finance, technology companies and AI research labs, as well as exceptional recent PhD graduates with strong practical experience. Prior financial markets experience is not essential.

Job Responsibilities

• Research and train deep learning models on large-scale order-book and market data to predict short-horizon market behaviour.
• Develop sequence and time-series models, exploring architectures and training methods suited to noisy, rapidly changing financial data.
• Own data preparation, feature and label construction, training pipelines and reproducible experiments.
• Evaluate models against robust baselines using time-aware out-of-sample validation, with careful controls for data leakage and overfitting.
• Improve training efficiency and scalability through GPU computing, distributed training and performance optimisation.
• Work closely with HFT researchers and engineers to optimise inference and integrate models into a latency-constrained C++ market-making system.
• Evaluate production model behaviour and use live feedback to guide further research.

Work Experience and Requirements

• A Master’s or PhD in Computer Science, Machine Learning, Mathematics, Physics, Electrical Engineering or a related quantitative discipline, or equivalent evidence of exceptional research and engineering ability.
• Typically 3–10 years of relevant post-degree experience. Exceptional recent PhD graduates with substantial hands-on model training and implementation experience are encouraged to apply.
• Personal ownership of training deep learning models at scale, demonstrated through datasets exceeding 100 GB or multi-GPU training.
• Strong Python skills and regular hands-on use of PyTorch or JAX, together with proficiency in C++, Rust or CUDA.
• Strong hands-on experience implementing and optimising deep learning models. Experience deploying models under latency or throughput constraints is preferred; recent PhD graduates may demonstrate relevant ability through research implementations, benchmarking or industry internships.
• A rigorous experimental research track record, demonstrated through first-author publications at leading ML conferences, Kaggle Master status or above, or industry research evaluated against credible baselines.
• Strong mathematical and statistical foundations, with the ability to design experiments, interpret results and assess model generalisation.
• Experience in sequence modelling, time-series prediction or efficient deep learning is particularly relevant.
• Custom CUDA / Triton kernels, distributed training, inference optimisation, or order-book and market microstructure experience would be advantageous.
• An interest in applying deep learning to financial markets. Prior trading experience is welcome but not required.

Application Process

Interested candidates should submit their resume to Tina Wang at [email protected], quoting “Machine Learning Engineer – Deep Learning for HFT” in the subject line. Please include relevant Google Scholar, GitHub or Kaggle links where available.

Only shortlisted candidates will be contacted.

License No: 24S2395

Registration No: R2090553