Machine Learning Engineer - Inference

About the role

A leading quantitative trading firm is seeking a Machine Learning Engineer specializing in inference to join a highly technical AI research team developing and deploying large-scale machine learning models for financial markets.

The team builds powerful foundation models for markets, trained on vast quantities of market and alternative data to predict future market behavior. These models are deployed directly into live trading environments, making inference speed, efficiency, and reliability critical to the business.

As a Machine Learning Engineer, you will work at the intersection of machine learning, high-performance computing, and systems engineering, with a broad mandate to improve large-scale model inference.

What You’ll Work On

Design and optimize high-performance inference systems for large-scale deep learning models

Develop and optimize GPU kernels using CUDA, Triton, Pallas, CuTe DSL, and related technologies

Improve lower-level performance across PyTorch, JAX, XLA, and CUDA Graphs

Explore and develop inference solutions across GPUs, ASICs, and FPGAs

Optimize data streaming and model-serving infrastructure for demanding real-time environments

Work closely with ML researchers to co-design efficient inference architectures

Improve latency, throughput, hardware utilization, and overall inference efficiency

Help shape the team’s broader machine learning and systems research agenda

The inference environment spans multiple platforms deployed globally and supports a variety of model architectures and trading strategies. The work is highly performance-sensitive, technically challenging, and has a direct impact on live trading performance.

Qualifications

2+ years of professional experience building deep learning or machine learning systems

Strong software engineering and systems fundamentals

Experience building deep learning systems in computationally intensive domains such as robotics, recommendation systems, biology, chemistry, physics, audio, video, or similar areas

Ability to translate techniques and approaches across different machine learning domains

Plus experience with one or more of the following:

CUDA, Triton, Pallas, or CuTe DSL kernel development

Lower-level PyTorch, JAX, or XLA development

CUDA Graphs

GPU performance optimization

FPGA or ASIC development

High-performance ML inference systems

Nice to Have

Experience with large-scale or low-latency model inference

LLM or foundation model experience

Experience optimizing GPU kernels or distributed ML workloads

Prior finance or trading experience is not required.