Member of Technical Staff, AI Training Infrastructure

About Us:At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.In the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice:Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. (blog)Open source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog)The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. (blog)The Role: As a Training Infrastructure Engineer, you'll design, build, and optimize the infrastructure that powers our large-scale model training operations. Your work will be essential to developing high-performance AI training infrastructure. You'll collaborate with AI researchers and engineers to create robust training pipelines, optimize distributed training workloads, and ensure reliable model development.Key Responsibilities:Design and implement scalable infrastructure for large-scale model training workloadsDevelop and maintain distributed training pipelines for LLMs and multimodal modelsOptimize training performance across multiple GPUs, nodes, and data centersImplement monitoring, logging, and debugging tools for training operationsArchitect and maintain data storage solutions for large-scale training datasetsAutomate infrastructure provisioning, scaling, and orchestration for model trainingCollaborate with researchers to implement and optimize training methodologiesAnalyze and improve efficiency, scalability, and cost-effectiveness of training systemsTroubleshoot complex performance issues in distributed training environmentsMinimum Qualifications:Bachelor's degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience3+ years of experience with distributed systems and ML infrastructureExperience with PyTorchProficiency in cloud platforms (AWS, GCP, Azure)Experience with containerization, orchestration (Kubernetes, Docker)Knowledge of distributed training techniques (data parallelism, model parallelism, FSDP)Preferred Qualifications:Master's or PhD in Computer Science or related fieldExperience training large language models or multimodal AI systemsExperience with ML workflow orchestration toolsBackground in optimizing high-performance distributed computing systemsFamiliarity with ML DevOps practicesContributions to open-source ML infrastructure or related projectsWhy Fireworks AI?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.