{"id":958,"url":"https://alion.io/job/figure-ai-staff-ai-inference-and-acceleration-engineer","title":"Staff AI Inference and Acceleration Engineer","company":{"id":25,"name":"Figure AI","domain":"figure.ai","url":"https://alion.io/company/figure-ai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":85,"open_postings":92,"ghost_share":0.163,"stale_share":0,"repost_share":0.011,"time_to_fill_p50_days":174,"computed_at":"2026-10-30T05:48:23Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Jose, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":180000,"max":275000,"currency":"USD","period":"year","gross":null,"usd_annual":275000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Apache TVM","optional":false},{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"JAX","optional":false},{"name":"LiteRT","optional":false},{"name":"MLIR","optional":false},{"name":"ONNX","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"ROCm","optional":false},{"name":"TensorRT","optional":false},{"name":"TFLite","optional":false},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-06-26T17:23:51Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-30T08:26:08Z","board_verified":true,"closed_at":null,"days_open":125,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":124},"description":"Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA.\nWe are looking for a Staff AI Inference & Acceleration Engineer to join the Platform Software team and own the on-board inference architecture for Figure’s humanoid robots. You will be the technical authority on how AI workloads are mapped, optimized, and executed across the robot’s compute hardware - driving down power consumption and cost while meeting the strict latency and reliability demands of a real-time autonomous system.\nResponsibilities:\nOwn the on-board inference architecture - mapping models to available accelerators (NPU, GPU, DSP, CPU) based on latency, power, and memory budgets.\n\nPartition inference workloads across heterogeneous compute resources, balancing real-time performance with power and thermal constraints.\n\nDefine and maintain a system-level compute budget across all inference tasks running on the robot.\n\nEvaluate next-generation acceleration hardware and contribute to the definition of future compute platform requirements.\n\nOptimize inference toolchains end-to-end - from model export through runtime execution - for target hardware.\n\nApply quantization (INT8, INT4, mixed-precision), pruning, operator fusion, and other compression techniques to reduce compute, memory, and power footprint.\n\nProfile inference pipelines to identify and eliminate bottlenecks in latency, memory bandwidth, and power consumption.\n\nOptimize kernel scheduling, memory layout, and data movement across the compute hierarchy.\n\nPartner closely with the AI/ML team to define model architecture constraints that are hardware-friendly from the outset.\n\nWork with the Platform Software team on runtime integration, scheduling, and power management.\n\nEngage with silicon vendors and research teams to track the accelerator landscape and influence hardware roadmaps.\n\nRequirements:\nM.S. or Ph.D. in Computer Engineering, Electrical Engineering, Computer Science, or a related field - or equivalent industry experience.\n\nAt least 8 years of industry experience in hardware acceleration, ML systems, or compute architecture.\n\nDeep understanding of AI/ML inference - model formats (ONNX, TFLite, etc.), inference runtimes, and deployment pipelines.\n\nHands-on experience optimizing models for edge or embedded hardware using quantization, pruning, and operator-level tuning.\n\nStrong understanding of computer architecture - memory hierarchies, data movement, and heterogeneous compute.\n\nExperience profiling and benchmarking inference workloads across CPU, GPU, NPU, DSP.\n\nFamiliarity with low-level toolchains and compilation frameworks (e.g. TVM, MLIR, TensorRT, Torch, SNPE/QNN, JAX, CUDA, ROCm).\n\nSolid software engineering skills in C++ and Python.\n\nStrong cross-functional communication skills - able to work effectively across hardware, software, and AI/ML teams.\n\nBonus Qualifications:\n Knowledge of real-time operating constraints and their impact on inference scheduling.\n\nTrack record of co-designing model architectures with ML teams to meet hardware constraints.\n\nThe US base salary range for this full-time position is $180,000 to $275,000 per year.\nThe pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. 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