Campus ML Research Engineer (Intern) at Jump Trading International Limited

Chicago, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

11 Feb, 26

Salary

300000.0

Posted On

13 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

Python, C++, Pytorch, JAX, Tensorflow, GPU Programming, CUDA, Triton, SYCL, ROCm, ML Systems, Statistical Analysis, Data Mining, Collaboration, Communication, Problem Solving

Industry

Financial Services

Description

Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Our trading teams are each comprised of a dynamic group of traders, quantitative researchers, and engineers who work together to examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis and data mining skills, using the results of their research to make forecasts and develop profitable predictive trading models. We are seeking world-class engineers to collaborate with our research, trading, engineering teams to build state-of-the-art ML systems that solve some of the most complex problems in quantitative finance. Whether optimizing training pipelines on high-performance computing clusters, developing low-latency inference systems, or pushing the boundaries of AI research from concept to production, you'll have the opportunity to work on impactful projects in a fast-paced, collaborative environment. If you are driven by technical challenges, eager to work with large-scale systems, and passionate about advancing ML capabilities, we want to meet you. What you'll do: Apply state-of-the-art techniques to complex and challenging domains. Work closely with researchers and quants to build flexible and reusable frameworks for financial ML. Optimize training pipelines to make the best use of our HPC resources. Integrate ML models into production systems where latency matters. Work across a mix of programming languages: C / C++ / Python / CUDA and other low-level GPU languages. Build large scale ML systems that are observable, performant, and flexible. Help improve productivity by reducing the iteration cycle time on research. Other duties as assigned or needed. Skills you'll need: This role covers a wide gamut of potential projects and skills. We don't expect everyone to have all of these, but for the applicable areas we are looking for deep technical expertise. Creative thinkers who are driven, self-motivated, and eager to solve challenging problems Proficiency in Python and/or C++ Proficiency in Pytorch, JAX, Tensorflow or other DL library. Ability to thrive in a collaborative, team-oriented environment Expertise in GPU or Accelerator programming (CUDA, Triton, SYCL, ROCm or equivalent) Experience building ML systems at large scale (hundreds of TBs of training data, low latency or high throughput inference requirements) Excellent written and verbal communication skills in English Reliable and predictable availability required INTERNATIONAL STUDENTS are encouraged to apply. We accept students eligible for CPT/OPT and we sponsor work visas for full-time positions. The estimated base salary for this role is $250,000 - $300,000 per year.

How To Apply:

Incase you would like to apply to this job directly from the source, please click here

Responsibilities

You will apply state-of-the-art techniques to complex domains and work closely with researchers and quants to build frameworks for financial ML. Additionally, you will optimize training pipelines and integrate ML models into production systems.