{"id":82106,"url":"https://alion.io/job/fireworks-member-of-technical-staff-performance-optimization","title":"Member of Technical Staff, Performance Optimization","company":{"id":18243,"name":"Fireworks AI","domain":"fireworks.ai","url":"https://alion.io/company/fireworks","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":85,"open_postings":15,"ghost_share":0,"stale_share":0.6,"repost_share":0,"time_to_fill_p50_days":59,"computed_at":"2026-11-04T06:34:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Mateo, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":175000,"max":220000,"currency":"USD","period":"year","gross":null,"usd_annual":220000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CUDA","optional":false},{"name":"HPC","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"ROCm","optional":false},{"name":"Triton","optional":false},{"name":"Fireworks AI","optional":true},{"name":"InfiniBand","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Structured Outputs","optional":true},{"name":"XLA","optional":true}],"status":"live","first_seen_at":"2025-05-06T20:04:42Z","employer_posted_date":"2025-05-06","last_verified_at":"2026-11-05T05:46:20Z","board_verified":true,"closed_at":null,"days_open":547,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":547},"description":"About Us:\nFireworks 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.\nThe Role:\nWe'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.\nYou’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.\nKey Responsibilities:\nOptimize system and GPU performance for high-throughput AI workloads across training and inference\n\nAnalyze and improve latency, throughput, memory usage, and compute efficiency\n\nProfile system performance to detect and resolve GPU- and kernel-level bottlenecks\n\nImplement low-level optimizations using CUDA, Triton, and other performance tooling\n\nDrive improvements in execution speed and resource utilization for large-scale model workloads (LLMs, VLMs, and video models)\n\nCollaborate with ML researchers to co-design and tune model architectures for hardware efficiency\n\nImprove support for mixed precision, quantization, and model graph optimization\n\nBuild and maintain performance benchmarking and monitoring infrastructure\n\nScale inference and training systems across multi-GPU, multi-node environments\n\nEvaluate and integrate optimizations for emerging hardware accelerators and specialized runtimes\n\nMinimum Qualifications:\nBachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience\n\n5+ years of experience working on performance optimization or high-performance computing systems\n\nProficiency in CUDA or ROCm and experience with GPU profiling tools (e.g., Nsight, nvprof, CUPTI)\n\nFamiliarity with PyTorch and performance-critical model execution\n\nExperience with distributed system debugging and optimization in multi-GPU environments\n\nDeep understanding of GPU architecture, parallel programming models, and compute kernels\n\nPreferred Qualifications:\nMaster’s or PhD in Computer Science, Electrical Engineering, or a related field\n\nExperience optimizing large models for training and inference (LLMs, VLMs, or video models)\n\nKnowledge of compiler stacks or ML compilers (e.g., torch.compile, Triton, XLA)\n\nContributions to open-source ML or HPC infrastructure\n\nFamiliarity with cloud-scale AI infrastructure and orchestration tools (e.g., Kubernetes)\n\nBackground in ML systems engineering or hardware-aware model design\n\nExample projects:\nImplement fully asynchronous low-latency sampling for large language models integrated with structured outputs\n\nImplement GPU kernels for the new low-precision scheme and run experiments to find optimal speed-quality tradeoff\n\nBuild a distributed router with a custom load-balancing algorithm to optimize LLM cache efficiency\n\nDefine metrics and build harness for finding optimal performance configuration (e.g. sharding, precision) for a given class of model\n\nDetermine and implement in PyTorch an optimal sharding scheme for a novel attention variant\n\nOptimize communication patterns in RDMA networks (Infiniband, RoCE)\n\nDebug numerical instabilities for a given model for a small portion of requests when deployed at scale\n\nWhy Fireworks?\nSolve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.\n\nBuild What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.\n\nOwnership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI-no bureaucracy, just results.\n\nLearn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.\n\nFireworks AI is an equal-opportunity employer. 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