Member of Technical Staff, New Grad (BS/MS)

About Us:

Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.

The Role

This role is built for new graduates who want to work on real AI systems, in production, from the start. As a MTS at Fireworks, you'll write and ship code that runs on one of the busiest inference platforms in the world — serving hundreds of models to developers and enterprises at massive scale.

We hire new grads into teams across Engineering, and we match you to a team based on your strengths and interests after we get to know you. Depending on where you land, your work could span inference and performance, model training and fine-tuning, distributed systems and cloud infrastructure, developer platform and APIs, or product and full-stack application engineering. One application puts you in front of all of them.

You'll join with a senior engineer as your mentor and a structured ramp-up: you'll start on well-scoped, real problems alongside your mentor, then take ownership of features and systems of your own. We move fast, our bar is high, and we'll invest in getting you there.

We welcome candidates who have completed a Bachelor's or Master's degree within the last 6 months, or expect to complete one by Summer 2027. Note that candidates graduating in December 2026 are highly preferred and prioritized.

What You'll Do

• Ship production code: Design, build, and deploy features and services that customers depend on every day
• Own problems end to end: Take a problem from ambiguous idea through design, implementation, testing, rollout, and operation
• Make things faster and more reliable: Improve the performance, efficiency, and scalability of the systems you work on
• Work with modern AI systems: Build with, on top of, or underneath large models — depending on your team, that could mean serving them, training them, evaluating them, or building products powered by them
• Debug hard things: Dig into unfamiliar code and unfamiliar failures, and get to the root cause
• Collaborate broadly: Work closely with engineers, researchers, product, and — on some teams — directly with customers

Minimum Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, completed within the last 6 months or before the end of 2026
• Strong programming fundamentals and solid grounding in data structures, algorithms, and systems
• Practical software engineering experience through internships, research, open-source contributions, or substantial personal projects
• Proficiency in at least one of Python, C++, Go, Rust, TypeScript, or a comparable language
• Fluency with AI coding harnesses and a habit of using them well
• Clear written and verbal communication, and the judgment to ask for help early
• Comfort with ambiguity, fast iteration, and high ownership

Preferred Qualifications

We don't expect any single candidate to have all of these.

• Hands-on experience building with LLMs or other ML models — coursework, projects, and internships all count
• Experience with GPU programming, CUDA, kernel optimization, or performance profiling
• Experience with distributed systems, Kubernetes, or cloud infrastructure (AWS/GCP/Azure)
• Experience with PyTorch, model training, fine-tuning (SFT, RLHF/RFT), or inference serving
• Experience shipping a full-stack or agentic application end to end, not just a notebook
• Familiarity with evaluation, RAG systems, or observability for ML systems
• Open-source contributions, competitive programming, or research publications