{"id":2653227,"url":"https://alion.io/job/d-matrix-senior-staff-inference-performance-engineer","title":"Senior Staff Inference Performance Engineer","company":{"id":8215,"name":"d-Matrix","domain":"d-matrix.ai","url":"https://alion.io/company/d-matrix","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":88,"open_postings":7,"ghost_share":0,"stale_share":0.286,"repost_share":0,"time_to_fill_p50_days":83,"computed_at":"2026-10-16T05:45:00Z"}},"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":"explicit","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":175000,"max":265000,"currency":"USD","period":"year","gross":null,"usd_annual":265000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"Linux","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"NCCL","optional":true},{"name":"Rust","optional":true},{"name":"SGLang","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-15T20:35:50Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-16T14:51:35Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration.\nWe value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? Together, we can help shape the endless possibilities of AI.\nSenior Staff Inference Performance Engineer\nLocation: Santa Clara, CA / Remote, US\nRole Overview\nThe Inference Cloud Engineering team builds the deployment and orchestration technology behind d-Matrix’s inference services: an internal cloud where teams test and tune models, and a customer-facing Cloud with self-service inference endpoints and compute. As the team’s Inference Performance Engineer, you will own end-to-end performance of the workloads we deploy, from the moment a request arrives to the moment a result is returned. The standard is that a workload on our orchestrated platform should perform at least as well as it does on bare metal. Today’s workloads include speculative decoding and attention-FFN disaggregation across d-Matrix Corsair accelerators and GPUs, with more heterogeneous architectures coming. Because there is no fixed system to master, the job is a method: profile first, form a hypothesis, test it, and then change the system. You will own the outcome, not every fix. Many root causes sit with other teams, so you will fix what you understand well and hand off the rest with evidence the owning team can act on.\nWhat You Will Do\nDiagnose performance problems across the full serving path, from orchestration (scheduling, placement, networking, autoscaling) to inference internals (batching, KV-cache, prefill/decode, serving framework, driver and runtime, interconnect, host OS and NUMA, accelerator).\n\nIsolate each problem to a specific layer and owner, then decide whether to fix it directly, route it to the owning team, or both, with a workaround now and a tracked handoff for the permanent fix.\n\nWrite minimal, reproducible problem statements for cross-stack findings so owning teams can act without repeating the investigation.\n\nProtect throughput, latency, and stability targets for orchestrated workloads by finding and closing gaps against bare metal, such as placement overhead, non-performance power defaults, and extra network hops.\n\nAssess what it takes to scale from single deployments to fleet operation, including routing, placement, and shared-resource contention.\n\nCapture each workload’s node requirements (CPU placement, memory and NUMA locality, network path) in the orchestration layer’s configuration so performant deployment is the default.\n\nBuild lightweight telemetry and benchmarks that reach into prototype stacks to isolate or hand off problems.\n\nTurn recurring investigations into playbooks and mentor engineers in the profiling-first method.\n\nWhat You Will Bring\n8+ years of hands-on performance or systems engineering, including resolving hard regressions that crossed multiple layers of a stack.\n\nTrack record of finding performance overhead through profiling in unfamiliar, sparsely documented systems, valued more than experience with any specific vendor stack or accelerator.\n\nDeep systems fundamentals, including Linux internals, NUMA, CPU pinning and cpusets, PCIe, memory and cache behavior, and network paths.\n\nExperience running performance-sensitive workloads on Kubernetes, including identifying when scheduler defaults, network overlays, or orchestration abstractions cost latency or throughput, and closing the gap.\n\nWorking knowledge of LLM inference serving, including batching, KV-cache, prefill/decode, quantization, and speculative decoding.\n\nProficiency in Python and C/C++, sufficient to instrument, debug, and patch code across the stack.\n\nClear technical writing that hardware, firmware, or platform teams can act on without context from your part of the stack.\n\nSound judgment about ownership, with examples of correctly handing off problems that were not yours to fix.\n\nFluency with AI and agentic tools for investigation, code search, log triage, and drafting instrumentation, with the discipline to verify output against real data.\n\nPreferred Qualifications\nExperience with LLM serving frameworks such as vLLM, SGLang, or TensorRT-LLM.\n\nHands-on experience building or tuning speculative decoding, disaggregated serving, or heterogeneous serving architectures.\n\nExperience tuning KV-cache management, request and batch scheduling, or weight prefetch and residency in an LLM serving system.\n\nGPU programming and profiling experience (such as CUDA, Nsight, NCCL), valued as transferable background.\n\nExperience with pre-production or bring-up hardware, or in organizations where root causes routinely cross team boundaries.\n\nRust experience.\n\nStartup experience or comfort in environments where priorities shift quickly.\n\nEqual Opportunity Employment Policy\nd-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.\nd-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.","description_format":"text","description_chars":6183,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","AI Compute & Inference","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-15T22:25:42Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 2 · green card filings: 1","filings_12m":2,"filings_prev_12m":24,"green_card_filings_12m":1,"median_offered_wage_usd":184167,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":1}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":83,"reasons":["conf:0","velocity","win:early"],"computed_at":"2026-10-16T05:45:00Z"},"pay":{"stated_usd_annual":265000,"is_top_pay":false},"html_url":"https://alion.io/job/d-matrix-senior-staff-inference-performance-engineer","json_url":"https://alion.io/job/d-matrix-senior-staff-inference-performance-engineer.json","meta":{"generated_at":"2026-10-16T16:54:03Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":1445,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":8215},"rest":"https://alion.io/mcp/rest/get_company?id=8215"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fd-matrix-senior-staff-inference-performance-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fd-matrix-senior-staff-inference-performance-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fd-matrix-senior-staff-inference-performance-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/d-matrix-senior-staff-inference-performance-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fd-matrix-senior-staff-inference-performance-engineer"}]}