Current Openings

The Role:  We're looking for a Software Engineer focused on Performance Optimization to help push the boundaries of speed and efficiency across our AI infrastructure. In this role, you'll take ownership of optimizing performance at every layer of the stack—from low-level GPU kernels to large-scale distributed systems. A key focus will be maximizing the performance of our most demanding workloads, including large language models (LLMs), vision-language models (VLMs), and next-generation video models. You’ll work closely with teams across research, infrastructure, and systems to identify performance bottlenecks, implement cutting-edge optimizations, and scale our AI systems to meet the demands of real-world production use cases. Your work will directly impact the speed, scalability, and cost-effectiveness of some of the most advanced generative AI models in the world. Key Responsibilities: Optimize system and GPU performance for high-throughput AI workloads across training and inference Analyze and improve latency, throughput, memory usage, and compute efficiency Profile system performance to detect and resolve GPU- and kernel-level bottlenecks Implement low-level optimizations using CUDA, Triton, and other performance tooling Drive improvements in execution speed and resource utilization for large-scale model workloads (LLMs, VLMs, and video models) Collaborate with ML researchers to co-design and tune model architectures for hardware efficiency Improve support for mixed precision, quantization, and model graph optimization Build and maintain performance benchmarking and monitoring infrastructure Scale inference and training systems across multi-GPU, multi-node environments Evaluate and integrate optimizations for emerging hardware accelerators and specialized runtimes Minimum Qualifications: Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience 5+ years of experience working on performance optimization or high-performance computing systems Proficiency in CUDA or ROCm and experience with GPU profiling tools (e.g., Nsight, nvprof, CUPTI) Familiarity with PyTorch and performance-critical model execution Experience with distributed system debugging and optimization in multi-GPU environments Deep understanding of GPU architecture, parallel programming models, and compute kernels Preferred Qualifications: Master’s or PhD in Computer Science, Electrical Engineering, or a related field Experience optimizing large models for training and inference (LLMs, VLMs, or video models) Knowledge of compiler stacks or ML compilers (e.g., torch.compile, Triton, XLA) Contributions to open-source ML or HPC infrastructure Familiarity with cloud-scale AI infrastructure and orchestration tools (e.g., Kubernetes, Ray) Background in ML systems engineering or hardware-aware model design Example projects: Implement fully asynchronous low-latency sampling for large language models integrated with structured outputs Implement GPU kernels for the new low-precision scheme and run experiments to find optimal speed-quality tradeoff Build a distributed router with a custom load-balancing algorithm to optimize LLM cache efficiency Define metrics and build harness for finding optimal performance configuration (e.g. sharding, precision) for a given class of model Determine and implement in PyTorch an optimal sharding scheme for a novel attention variant Optimize communication patterns in RDMA networks (Infiniband, RoCE) Debug numerical instabilities for a given model for a small portion of requests when deployed at scale

The Role: As a Research Scientist focused on Reinforcement Learning (RL), you’ll apply your deep expertise in the field to push the boundaries of how large language models are trained, aligned, and deployed. We’re looking for someone with a strong foundation in RL - not just familiarity, but hands-on experience designing algorithms, building training pipelines, and running experiments. You’ll work on everything from scalable RLHF alternatives (e.g., GRPO, DPO) to reward modeling and agent-based training. Your contributions will directly impact Fireworks’ model quality, training workflows, and customer-facing APIs. You’ll also collaborate with researchers, engineers, and product teams to translate state-of-the-art RL into practical systems used by companies deploying LLMs at scale. Key Responsibilities: Design, implement, and optimize reinforcement learning algorithms to improve the training and alignment of large language models. Develop scalable pipelines for reinforcement learning from human feedback (RLHF) and explore alternatives such as GRPO and DPO. Conduct hands-on experiments across reward modeling, agent-based training, and reinforcement fine-tuning of LLMs. Collaborate with cross-functional teams, including researchers, engineers, and product managers, to integrate cutting-edge RL advancements into production systems. Analyze experimental results and iterate quickly to improve model performance and training workflows. Contribute to the development of Fireworks’ customer-facing APIs by enhancing model alignment and real-world usability. Stay current with the latest research in reinforcement learning, LLM alignment, and AI safety to inform and inspire new initiatives. Minimum Qualifications: 5+ years of research experience specifically in reinforcement learning Strong understanding of RL fundamentals, including policy gradients, actor-critic methods, offline RL, and preference-based learning Experience with reinforcement fine-tuning of LLMs (e.g., PPO, DPO, GRPO) Experience building and training deep learning models using PyTorch Proficiency in Python and ability to write clean, efficient, research-grade code Demonstrated ability to lead RL experiments from idea to implementation and analysis Excellent communication skills and the ability to collaborate in fast-paced, cross-functional environments Preferred Qualifications: PhD in Computer Science, Machine Learning, Applied Mathematics, or a related field Publications at top-tier ML conferences (NeurIPS, ICML, ICLR, etc.) Experience building interactive agents that leverage tools, APIs, or search Expertise in reward modeling and LLM evaluation strategies