Machine Learning Engineering Internship: Summer 2027

Susquehanna International Group, LLP; Hong Kong; On-site; HKD 167,000 - 279,000; Full time

Job summary

Susquehanna International Group, LLP is offering a 10-week Machine Learning Engineering Internship in Hong Kong, designed for students eager to build systems at the intersection of ML, data, and markets.

You will train and deploy ML pipelines on our GPU infrastructure, contribute to research and trading systems, and learn from mentors while exploring distributed training, inference, and data ingestion.

Qualifications

• Pursuing Bachelor's, Master's, or PhD in a technical field with near-term graduation.
• Strong Python programming skills and comfort with C++.
• Hands-on experience with PyTorch or JAX at scale.
• Experience with distributed training, GPU programming, or large-scale data processing.
• Projects or open-source work detailing design and implementation.

Responsibilities

• Build and optimize training pipelines across GPU infrastructure.
• Work on inference and deployment in production-like trading systems.
• Develop data infrastructure for large market datasets.
• Profile, benchmark ML workloads, contribute to libraries/tools.
• Receive one-on-one mentorship and participate in an education program.

Skills

Python

C++

PyTorch

JAX

GPU programming

Distributed training

Data processing

Algorithms

Open-source contribution

Education

Bachelor's/Master's/PhD in CS/ML/EE/Math/Physics/Stats

Tools

CUDA

Triton

CuTe DSL

Job description

Overview

Our Machine Learning Engineering Internship is a 10-week immersive experience designed for students who are passionate about building the systems at the intersection of machine learning, large-scale data, and markets.

As a Machine Learning Engineering Intern, you'll work on high-impact projects that closely reflect the challenges and workflows of our full-time engineering team. You'll apply your software engineering skills to real machine learning systems while developing a deep understanding of how machine learning integrates into Susquehanna's research and trading systems. A model is useful only if it can be trained quickly, fed reliably, and run fast enough to act on - our engineers own the systems that make that true, and we scopeintern projects the same way.

Susquehanna's proprietary datasets and computing infrastructure - including a rapidly growing cluster of thousands of high-end GPUs - support computationally intensive training, simulation, and rapid experimentation. Engineering teams here are small and highly collaborative, ideas are debated openly, and work that proves out reaches production quickly.

Successful intern candidates will participate in a U.S. based internship with full-time placement in our Hong Kong office upon graduation.Visa sponsorship is available for this position.

What You Can Expect

• Build and optimize training pipelines that run across our GPU infrastructure, including distributed training for large models
• Work on inference and deployment — the latency, throughput, and cost of models running in production trading systems
• Develop the data infrastructure behind our research, moving large and noisy market datasets through ingestion, storage, and transformation
• Profile and benchmark machine learning workloads, and contribute to the internal libraries and open-source tools our researchers rely on every day
• One-on-one mentorship from experienced engineers and researchers
• Participate in a comprehensive education program with deep dives into Susquehanna's ML, quant, and trading practices
• No prior finance background required

What we’re looking for

• Currently pursuing a Bachelor's, Master's, or PhD in computer science, machine learning, electrical engineering, mathematics, physics, statistics, or a related technical field. Intention to graduate and begin full time employment by August 2028
• Strong programming skills in Python, working comfort in a systems language such as C++, a plus
• Hands-on experience with machine learning frameworks such as PyTorch or JAX that goes beyond calling the API - training at scale, extending a library, or making an existing workflow measurably faster
• Exposure to the systems machine learning runs on: distributed training, GPU programming, orchestration, or large-scale data processing. We're more interested in what you did with a tool and what it changed than in seeing it named on a list
• Experience with GPU kernel programming in CUDA, Triton, or CuTe DSL
• Solid computer science fundamentals: data structures, algorithms, concurrency, and an understanding of how software behaves on real hardware
• A project, system, or open-source contribution you can walk through in detail - what you designed, what you decided, and what you specifically did
• Deep interest in solving complex problems and a drive to innovate in a fast-paced, competitive environment