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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.

1f:T1d8a,

About Us:

THE ROLE:

This role is designed for systems researchers finishing their PhD who want to see their ideas run on real fleets at real scale. As a Member of Technical Staff on the Systems Infrastructure team, you'll design and build the substrate underneath Fireworks — the schedulers, storage systems, and networks that keep tens of thousands of accelerators busy and inference latency low.

The problems here are the ones your dissertation probably touched: how to place compute-intensive jobs across heterogeneous hardware without stranding capacity, how to move model weights and KV cache fast enough that they never become the bottleneck, and how to keep a datacenter network saturated with collective traffic without collapsing tail latency. The difference is that here you get a production fleet as your testbed and your work ships.

You'll be paired with a senior engineer as a mentor and given a real problem from day one. Start dates are flexible around thesis defense timelines.

AREAS OF FOCUS

We're hiring across three areas — depth in any one of them is what we're looking for, not all three:

Scheduling & resource management: GPU job scheduling and scheduling of compute-intensive workloads across heterogeneous hardware; multi-tenant isolation, fair sharing and preemption, topology- and locality-aware placement, autoscaling, fleet utilization, and capacity planning across accelerator generations and vendors
Distributed storage & caching: High-performance distributed storage and caching for model weights, checkpoints, datasets, and KV cache; tiering across memory, local NVMe and object storage, cache admission and eviction policy, consistency, and fast cold-start and weight-loading paths
Datacenter networking: High-performance DC networks for AI workloads; RDMA/RoCE and InfiniBand, collective communication (NCCL/RCCL) performance, congestion control, topology design, load balancing, and tail-latency and reliability engineering at fleet scale

KEY RESPONSIBILITIES

Design, build, and operate core infrastructure systems for large-scale training and inference
Model and measure system behavior — build the benchmarks, traces, and simulators needed to reason about scheduling, caching, and network performance before committing to a design
Identify bottlenecks across the stack, from kernel and driver to scheduler policy, and drive them out with data
Turn research ideas into production systems that hold up under real workloads, real failures, and real customers
Work closely with the research and inference teams so that infrastructure design and model design inform each other
Contribute to the team's technical direction by tracking emerging hardware, interconnects, and systems research

MINIMUM QUALIFICATIONS:

PhD completed within the last 6 months, or expected completion by December 2026, in Computer Science, Computer Engineering, Electrical Engineering, or a similar field
Research background in one or more of: distributed systems, operating systems, scheduling and resource management, storage systems, computer networks, computer architecture, or high-performance computing
Depth in at least one of the three focus areas above, demonstrated through your dissertation, publications, or systems you've built
Strong systems programming skills in C/C++, Rust, Go, or a similar language, plus working proficiency in Python
Experience building and evaluating real systems — not only simulation — and reasoning rigorously about performance with measurements
Ability to communicate systems design and results clearly to audiences with different backgrounds

PREFERRED QUALIFICATIONS:

First-authored publications at top-tier systems or networking venues (OSDI, SOSP, NSDI, EuroSys, SIGCOMM, ATC, FAST, ASPLOS, MLSys, SC, or similar)
Industry internships in infrastructure, cloud, or HPC, or meaningful open-source contributions to systems projects (Kubernetes, Ray, Slurm, vLLM, NCCL, DPDK/SPDK, Ceph, or similar)
Hands-on experience with GPU clusters, accelerator programming (CUDA, ROCm, Triton), or heterogeneous compute environments
Experience with Kubernetes and cloud infrastructure, or with operating multi-tenant clusters in production
Experience with performance profiling and debugging in distributed environments
Research or engineering experience demonstrated via grants, fellowships, patents, or systems competitions

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.

20:T1531,

About Us:

THE ROLE:

This role is designed for researchers finishing their PhD who want their work to reach production, not just publication. As a Member of Technical Staff on the Research team, you'll push the boundaries of generative AI, advancing LLMs and multimodal systems through foundational research. Your work will enhance model efficiency, accuracy, and scalability, directly shaping our high-performance AI infrastructure.

You'll be paired with a senior researcher as a mentor and given a real research problem from day one. You'll collaborate with top experts in deep learning, distributed systems, and optimization, and you'll see the models you help create deployed by some of the world's leading companies — often within weeks rather than years.

We welcome candidates who have completed within the last 6 months, or expected completion by December 2026.

KEY RESPONSIBILITIES

Conduct foundational research to advance the capabilities, efficiency, and reliability of LLMs and multimodal systems
Design, implement, and evaluate novel model architectures, training methods, and optimization techniques
Work alongside engineering teams to transition research prototypes into production-grade systems
Analyze empirical results, identify performance bottlenecks, and iterate quickly to improve model quality
Bring ideas and emerging directions from your doctoral research into our internal research agenda

MINIMUM QUALIFICATIONS:

PhD completed within the last 6 months, or expected completion by December 2026, in Computer Science, Machine Learning, Computational Physics, Mathematics, Electrical Engineering, or a similar field
Research background in Artificial Intelligence, Machine Learning, Physics, or a related area, demonstrated through your dissertation, publications, or research projects
Experience solving analytical problems using analytic and quantitative approaches
Experience communicating research to audiences with different backgrounds
Proficiency coding in Python, C/C++, or other similar languages
Hands-on experience with ML frameworks such as PyTorch, JAX, or TensorFlow

PREFERRED QUALIFICATIONS:

First-authored publications at top-tier peer-reviewed conferences or journals (NeurIPS, ICML, ICLR, MLSys, or similar)
Industry research internships, or open-source contributions to ML systems, model training, or inference projects
Research or engineering experience demonstrated via grants, fellowships, patents, or coding competitions
Experience with large-scale model training, distributed systems, GPU kernel development, or inference optimization
Experience taking research code beyond the prototype stage

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.

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Role	Company	Location	Season	Posted
Member of Technical Staff, New Grad (BS/MS)	Fireworks AI	San MateoHybrid	Summer 2027	1 month ago
Member of Technical Staff, Systems Infrastructure (2026 PhD New Grad)	Fireworks AI	San MateoHybrid	Summer 2027	1 month ago
Member of Technical Staff, Research (2026 PhD New Grad)	Fireworks AI	San MateoHybrid	Summer 2027	1 month ago