Requirements

Collaborate closely with quantitative researchers, platform engineers, and other cross-functional teams to understand research workflows and translate them into scalable, reliable platform capabilities
Design, build, and maintain distributed services and APIs that manage compute workloads, data access, and execution lifecycles across HPC environments
Improve the usability, reliability, and performance of large-scale quantitative research workflows, with a strong focus on developer experience and operational excellence
Contribute to the design and implementation of scheduling, security, and observability solutions suitable for highly parallel, compute-heavy systems
Work with state-of-the-art infrastructure and modern software engineering practices to support massive compute pipelines and rapidly evolving research demands
Take ownership of complex technical problems, operating with a high degree of autonomy while making pragmatic trade-offs to deliver impact quickly and safely
BSc / MEng / PhD in Computer Science or a closely related field, or equivalent practical experience, with 5+ years of professional software development
Strong proficiency in at least one statically-typed programming language (e.g. C, C++, Java, Rust), with an interest in Go (prior experience welcome but not required). Experience with Python is a plus
5+ years of experience designing, implementing, and operating large-scale distributed systems in production environments

Nice to Haves

Prior experience in quantitative finance or working closely with quantitative researchers and data-intensive workloads
Understanding of GPU programming models and performance considerations (e.g. CUDA, memory hierarchies, parallel execution), with exposure to ML frameworks such as PyTorch or TensorFlow
Solid understanding of systems concepts such as concurrency, performance, fault tolerance, and reliability
Comfortable working in fast-moving, rapidly evolving environments; proactive in learning new technologies and improving existing systems
Pragmatic engineering mindset, with a focus on delivering high-impact solutions while maintaining long-term platform health

Collaborating closely with quantitative researchers, platform engineers, and other cross-functional teams
Designing, building, and maintaining distributed services and APIs
Improving the usability, reliability, and performance of large-scale quantitative research workflows
Contributing to the design and implementation of scheduling, security, and observability solutions
Working with state-of-the-art infrastructure and modern software engineering practices
Taking ownership of complex technical problems with autonomy