{"id":3002527,"url":"https://alion.io/job/nebius-staff-machine-learning-engineer-llm-inference-optimization","title":"Staff Machine Learning Engineer, LLM Inference Optimization","company":{"id":6364,"name":"Nebius","domain":"nebius.com","url":"https://alion.io/company/nebius","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":110,"ghost_share":0,"stale_share":0.618,"repost_share":0,"time_to_fill_p50_days":59,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Amsterdam, Netherlands","Berlin, Germany","London, United Kingdom","Prague, Czech Republic","Zurich, Switzerland","Poland"],"countries":["NL","DE","GB","CZ","CH","PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":197000,"max_usd":448000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1192},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Knowledge Distillation","optional":false},{"name":"KServe","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"Model Distillation","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Ray","optional":false},{"name":"Ray Serve","optional":false},{"name":"SGLang","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"TPOT","optional":false},{"name":"Triton","optional":false},{"name":"Triton Inference Server","optional":false},{"name":"vLLM","optional":false},{"name":"VLM","optional":false},{"name":"AWQ","optional":true},{"name":"Function Calling","optional":true},{"name":"GPTQ","optional":true},{"name":"Structured Outputs","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-10-21T12:25:43Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-24T08:52:35Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About Nebius:\nNebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.\nBuilt by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.\nListed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.\nRole Summary\nNebius Token Factory is building production-grade inference for open-source, customer, and internally optimized models. This role owns the path from model artifact to fast, reliable, cost-efficient endpoint. The work sits at the intersection of model internals, inference engines, serving architecture, benchmarking, and production deployment.\n\nWhat You Will Do\nOwn a platform-level optimization pillar such as quantization, speculative decoding, KV-cache optimization, engine selection, or long-context serving.\n\nCreate reusable evaluation, benchmarking, and rollout frameworks across multiple model families.\n\nProvide technical direction to MLEs, Applied Scientists, GPU kernel engineers, and infrastructure engineers on shared performance goals.\n\nOptimize LLM and VLMendpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token.\n\nDeploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIADynamo, or similar systems.\n\nBuild and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery.\n\nImplement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving.\n\nBuild reproducible benchmark harnesses for TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token.\n\nPartner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers.\n\nWrite clear design docs, performance reports, rollout plans, and customer-facing technical explanations.\n\nRequired Qualifications\nStrong Python and PyTorch engineering skills.\n\nHands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference systems.\n\nPractical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIADynamo, Ray Serve, KServe, or equivalent internal systems.\n\nStrong understanding of transformer inference bottlenecks, including KVcache, attention, memory bandwidth, batching, parallelism, and long-context serving.\n\nAbility to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs.\n\nStrong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams.\n\nPreferred Qualifications\nExperience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, SmoothQuant, or related techniques.\n\nExperience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods.\n\nExperience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration.\n\nCUDAor Triton familiarity, even if the role is not primarily a kernel-engineering role.\n\nOpen-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects.\n\nBenefits & Perks:\nCompetitive compensation\nCareer growth and learning opportunities\nFlexibility and ownership\nCollaborative and innovative culture\nOpportunity to work on impactful AI projects\nInternational environment and talented teams\nWhat's it like to work at Nebius:\nFast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI \nEqual Opportunity Statement:\nNebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.\nApplicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. \nIf you need accommodations during the application process, please let us know.","description_format":"text","description_chars":5123,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Data Centers & Colocation","Cloud Platforms (IaaS & PaaS)","AI Compute & Inference"],"lifecycle":[{"event":"open","at":"2026-10-21T12:41:21Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":2,"expected_fill_days":59,"reasons":["conf:1","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-10-24T06:02:35Z"},"pay":null,"html_url":"https://alion.io/job/nebius-staff-machine-learning-engineer-llm-inference-optimization","json_url":"https://alion.io/job/nebius-staff-machine-learning-engineer-llm-inference-optimization.json","meta":{"generated_at":"2026-10-24T10:52:58Z","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":15483,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-25T00:00:00Z"}},"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":6364},"rest":"https://alion.io/mcp/rest/get_company?id=6364"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fnebius-staff-machine-learning-engineer-llm-inference-optimization"},{"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%2Fnebius-staff-machine-learning-engineer-llm-inference-optimization"},{"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%2Fnebius-staff-machine-learning-engineer-llm-inference-optimization"},{"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/nebius-staff-machine-learning-engineer-llm-inference-optimization\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fnebius-staff-machine-learning-engineer-llm-inference-optimization"}]}