ML Engineer (High-Frequency Trading)

TL;DR

ML Engineer (High-Frequency Trading): Optimizing large-scale model training and compute infrastructure for an algorithmic trading firm with an accent on GPU kernels, system-level throughput, and distributed systems techniques. Focus on reducing training time, boosting iteration speed, and improving compute efficiency for high-frequency trading algorithms.

Company

Pinely is a privately owned and funded algorithmic trading firm focused on developing and using in-house HFT algorithms on global financial markets.

What you will do

• Optimize model training pipelines to improve speed and reliability for faster experimentation.
• Apply GPU-level optimization techniques using JAX, Triton, and low-level CUDA to enhance training performance.
• Identify and resolve performance bottlenecks across the entire ML pipeline, from data loading to CUDA kernels.
• Build tools and extend internal infrastructure to support scalable and high-performance training workflows.
• Mentor and support engineers and researchers in adopting performance best practices.
• Help grow the team’s GPU and systems-level capabilities, fostering engineering excellence.

Requirements

• Demonstrated experience optimizing neural network training in production or large-scale research settings.
• Extensive practical experience with ML frameworks such as PyTorch or JAX.
• Hands-on experience with training and optimizing deep learning architectures like LSTM and Transformer-based models.
• Experience working with CUDA, Triton, or other low-level GPU technologies for performance tuning.
• Proficiency in profiling and debugging training pipelines using tools like Nsight/cprofiler/CUDA/gdb/torch profiler.
• Understanding of distributed training concepts (e.g., data/model/tensor/sequence/pipeline parallelism).
• Strong proficiency in Python for building infrastructure-level tooling and debugging training systems.

Culture & Benefits

• High base salary and generous bonus structure with flexible discussion of employment conditions.
• Access to cutting-edge hardware and software, leveraging high technical expertise for bold ideas.
• Ownership over initiatives that directly solve business problems.
• Flexible workflow with a lack of formalism, bureaucracy, and over-management.
• Tuition reimbursement and sponsorship for conferences and training.