Member of Technical Staff, New Grad (BS/MS)
Fireworks AI is hiring a Member of Technical Staff, New Grad (BS/MS) in San Mateo, United States. It pays $160k-$180k a year and Level rates it ; you can apply on Fireworks AI.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- Tools and skills the posting cites include PyTorch, Python and RAG.
- Level classifies it as a junior role.
- Level lists 90 other open roles at Fireworks AI.
AI in this role
Build and ship production code, distributed systems, and inference infrastructure for state-of-the-art AI models as a new grad MTS.
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
Why Fireworks?
Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.
Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
How we rate this
Member of Technical Staff, New Grad (BS/MS) at Fireworks AI rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- What are the limits of PyTorch that you've run into, and how did you work around them?
- What's a project where you used Python hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: RAG, Fine-tuning, PyTorch, and Python. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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