Member of Technical Staff (New Grad (Bachelor of Science/Master of Science))

Join Fireworks as a Member of Technical Staff (MTS) and work on real AI systems in production. As a new graduate, you'll write and ship code that runs on a busy inference platform, serving hundreds of models to developers and enterprises at scale. You'll be matched to a team based on your strengths and interests, and your work could span various areas such as inference and performance, model training and fine-tuning, distributed systems and cloud infrastructure, developer platform and APIs, or product and full-stack application engineering. You'll receive mentorship from a senior engineer and have the opportunity to take ownership of features and systems.

• Design, build, and deploy features and services that customers depend on every day.
• Take a problem from ambiguous idea through design, implementation, testing, rollout, and operation.
• Improve the performance, efficiency, and scalability of the systems you work on.

• Practical software engineering experience through internships, research, open-source contributions, or substantial personal projects
• 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
• Proficiency in at least one of Python, C++, Go, Rust, TypeScript, or a comparable language
• 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
• Comfort with ambiguity, fast iteration, and high ownership
• 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 shipping a full-stack or agentic application end to end, not just a notebook
• Experience with PyTorch, model training, fine-tuning (SFT, RLHF/RFT), or inference serving
• Familiarity with evaluation, RAG systems, or observability for ML systems
• Open-source contributions, competitive programming, or research publications