Software Engineer (2027 Graduate)

Software Engineer

Build technology where every nanosecond matters.

At Graviton, software isn't just a tool that supports trading. It is the infrastructure behind every research breakthrough, every trading decision and every competitive advantage.

As a Software Engineer, you'll work on systems where performance, reliability and precision matter at an extraordinary scale. You'll partner closely with software engineers and quantitative researchers to build technology that processes enormous volumes of market data, powers quantitative research and supports live trading.

You'll take on problems that don't have obvious answers — from designing high-performance systems and distributed infrastructure to eliminating bottlenecks measured in microseconds and building tools that make our researchers and engineers faster.

Your work will go into production, be measured against real-world performance and have a direct impact on how our trading systems operate.

If you enjoy solving hard engineering problems, understanding systems at a deep level and pushing technology to its limits, you'll feel right at home.

What You'll Work On

You'll work across the engineering stack that powers our quantitative research and trading platforms.

Depending on your team, your work may include:

• Designing and building high-performance, low-latency systems in modern C++.
• Building distributed systems that process and analyze massive volumes of market data.
• Designing systems where latency, throughput and reliability directly influence trading performance.
• Working on Linux systems, networking, concurrency and multithreaded applications.
• Profiling systems, identifying bottlenecks and optimizing performance at the hardware and software level.
• Building robust infrastructure that supports quantitative research and live trading.
• Debugging complex production systems and solving problems where correctness and reliability are critical.
• Designing internal platforms and developer tools that accelerate research and engineering productivity.
• Exploring AI-powered developer tools and intelligent automation to improve engineering workflows.
• Collaborating with quantitative researchers to turn ideas and models into reliable production systems.
• Participating in architecture discussions, technical design reviews and code reviews.
• Taking ownership of systems from design and implementation through deployment, monitoring and continuous improvement.

The problems are hard. The systems are real. And the impact is measurable.

The Engineering Challenge

At Graviton, you'll encounter engineering problems where traditional solutions aren't always good enough.

You'll think about questions like:

• How do you make a system faster without compromising correctness?
• How do you process enormous amounts of data without introducing unnecessary latency?
• How do you design software that remains reliable under extreme workloads?
• How do you find a bottleneck that exists for only a few microseconds?
• How do you build infrastructure that enables researchers to experiment faster?
• How do you balance performance, simplicity and maintainability in systems that operate at extraordinary speed?

You'll be expected not just to write code, but to understand the system, challenge assumptions and find better ways to solve the problem.

Ownership From Day One

At Graviton, engineers are trusted with meaningful problems early.

You'll work closely with experienced engineers and quantitative researchers, receive thoughtful technical feedback and have the opportunity to own projects end-to-end.

That means going beyond implementation:

Understand the problem → design the solution → build it → measure it → improve it → take it to production.

You'll participate in code reviews, architecture discussions and technical design sessions, while learning from people who have spent years building high-performance systems.

What Success Looks Like

Over time, successful engineers at Graviton:

• Own critical systems and engineering projects end-to-end.
• Build software that directly supports quantitative research and live trading.
• Solve complex performance, scalability and reliability challenges.
• Make systems faster, simpler and more robust.
• Develop a deep understanding of the systems they build — from application code down to hardware and networking.
• Challen