Jobs / United States / Sambanova Systems INC
Senior AI Performance Engineer
Sambanova Systems INC · 🇺🇸 San Jose, California, United States
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 23 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 4 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Sambanova Systems INC paid sponsored hires in similar roles8 certified filings for “Principal Engineer” (Computer and Information Research Scientists) in CA: $175k–$190k, median $183k. Most were filed at wage level II (100%) — 2 lottery entries, ≈31% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
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Sponsor Radar — Sambanova Systems INC
The US Department of Labor certified 23 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 4 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Sambanova Systems INC →
About the role
SambaNova is a leader in next-generation AI infrastructure, delivering a full-stack inference platform for customers worldwide. At the core of SambaNova's technology is the RDU (Reconfigurable Dataflow Unit) — a chip built on a dataflow architecture rather than the traditional GPU model. Its decode performance is especially strong for agentic workloads like multi-turn agents, code generation, and long-running applications. RDUs are packaged into SambaRack, rack-scale hardware that lets customers deploy state-of-the-art models with better performance, greater energy efficiency, and faster time to value. About the role We are seeking a talented and driven ML performance engineer to optimize and scale state-of-the-art foundation models on SambaNova's reconfigurable dataflow platform. You'll work hands-on with some of the most advanced models in the world — such as DeepSeek R1, GPT OSS, and other frontier architectures — to push the limits of throughput, latency, and efficiency. In this role, you'll bridge the gap between deep learning and systems performance, collaborating across compiler, runtime, and hardware layers to deliver world-record performance for large-scale AI inference. Responsibilities • Bring up and optimize cutting-edge foundation models (e.g., DeepSeek, Llama, Qwen, and others) on the SambaNova platform through the SambaNova software stack. • Profile and enhance model performance across compiler, runtime, and hardware layers to achieve SOTA throughput and latency. • Collaborate with machine learning, compiler, runtime, and hardware teams to deliver co-designed, high-performance AI applications. • Integrate the latest advances in model architecture, quantization, scheduling, and memory optimization from both academia and industry. • Develop robust, scalable, and efficient end-to-end inference solutions aligned with customer needs. • Identify performance bottlenecks and propose dataflow or scheduling optimizations for both single-node and distributed systems. Required Qualifications • Bachelor's or higher degree in computer science, electrical engineering, or a related field (e.g., applied mathematics, physics, or statistics). • 3+ years of experience in one or more of the following areas: • Deep learning model development and performance optimization • Compiler, runtime, or kernel-level optimization • Software–hardware co-design or systems performance tuning • Proficiency in Python or C++, with strong foundations in algorithms, data structures, and numerical computing. • Experience with at least one major ML framework — PyTorch, TensorFlow, or JAX. • Demonstrated ability to analyze and optimize performance in real-world ML pipelines. Preferred Qualifications • Hands-on experience with LLM or multimodal model training and inference. • Background in large-scale distributed training, continuous batching, and high-throughput inference systems. • Familiarity with quantization, graph optimization, kernel fusion, and model partitioning. • Experience with frameworks such as DeepSpeed, Megatron, vLLM, or TensorRT. • Strong GPU programming skills (CUDA, Triton, or OpenCL); experience with cuDNN, cuBLAS, or similar libraries is a plus. • Knowledge of memory hierarchy optimization, caching, and scheduling for large-scale model execution. • Publication record or open-source contributions in ML systems or performance optimization is a plus. Base Salary Range: Base Pay Range $180,000 — $255,000 USD Submission Guidelines Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified. EEO Policy SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified ap