{"id":2500268,"url":"https://alion.io/job/farcana-lead-inference-engineer","title":"Lead Inference Engineer","company":{"id":4430105,"name":"Farcana","domain":"farcana.com","url":"https://alion.io/company/farcana","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"PeopleForce","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United Arab Emirates"],"countries":["AE"],"hiring_countries":["AE"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":70000,"max_usd":169000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1041},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"Quantization","optional":false},{"name":"SGLang","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-10-14T05:41:15Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-14T17:46:02Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"01. ABOUT THE COMPANYDizzaract is a product-driven company operating at the intersection of gaming, digital platforms, and AI. We build and scale multiple products, including FAR Labs & Gamed - each exploring a different space, yet united by a shared approach: moving fast, staying curious, and focusing on things that people actually use.\nWe operate as a collaborative, non-hierarchical team where ideas are valued based on their impact, not their origin, and where AI is embedded across everything we build, from infrastructure to product decisions.\n02. ABOUT THE ROLE\nFAR Labs is building a distributed inference platform designed to serve large language models efficiently across diverse hardware. Serving quality - latency, throughput, memory efficiency, cost, and model quality - sits at the core of the product.\nWe’re looking for a Lead Inference Engineer to own the technical direction of our inference stack and how we measure its performance.\nYou will work across inference serving, GPU performance, distributed systems, benchmarking, and model optimisation to make our serving infrastructure faster and more efficient while maintaining a clear quality bar.\nThis is the senior technical seat for AI serving at FAR Labs. You will set the serving and measurement roadmap, establish engineering standards, and guide engineers working across serving, metrics, and testing.\nThe role is highly hands-on. You’ll be expected to work directly with the inference stack, identify performance bottlenecks, implement improvements, and demonstrate their impact through rigorous and reproducible measurement.\n03. WHO YOU ARE\nInference Expert: You have deep hands-on experience with LLM inference serving and understand what happens between a model being loaded and a production-grade token being served.\nPerformance Engineer: You think naturally in terms of latency, throughput, memory bandwidth, GPU utilisation, model quality, and cost - and understand how changes across the stack affect each of them.\nGPU-Native: You are comfortable working close to the hardware with CUDA or Triton and understand modern GPU architectures, memory behaviour, and precisions such as FP8 and FP4.\nDistributed Systems Thinker: You understand the challenges of routing, scheduling, communication, caching, and workload distribution across heterogeneous compute infrastructure.\nMeasurement-Driven: You care about proving performance improvements properly. You understand load generation, latency percentiles, throughput-at-SLO, reproducibility, and the importance of measuring speed alongside quality.\nTechnical Leader: You have previously led an inference, serving, or similarly complex technical initiative and can set direction that strong engineers can execute against.\nBuilder: You enjoy solving technically difficult problems in environments where the architecture, tooling, benchmarks, and engineering standards are still evolving.\n04. RESPONSIBILITIESInference Serving: Own and improve the FAR Labs inference serving stack, including prefill/decode disaggregation, continuous batching, KV-cache management, cache-aware routing, speculative decoding, and other serving optimisations.\nServing Performance: Improve throughput, latency, memory efficiency, and serving cost while maintaining clearly defined model-quality standards.\nGPU Performance Engineering: Identify and eliminate GPU performance bottlenecks across the inference stack using CUDA, Triton, profiling, kernel optimisation, memory optimisation, and appropriate precision strategies.\nMixture-of-Experts Serving: Drive efficient serving of large MoE models, including expert parallelism, all-to-all communication, expert-load balancing, and distributed execution.\nQuantization & Model Optimisation: Implement and evaluate quantization and other model optimisation techniques while measuring accuracy against appropriate full-precision references.\nBenchmarking & Measurement: Own rigorous, reproducible benchmarking across serving performance, quality, and cost. Establish measurement methodologies that can withstand external technical scrutiny.\nPerformance Instrumentation: Build and improve the instrumentation required to understand system behaviour, identify bottlenecks, compare configurations, and validate improvements.\nDistributed Serving: Make technical decisions around routing, scheduling, caching, and workload distribution across diverse hardware environments.\nTechnical Roadmap: Define the AI serving and measurement roadmap and determine which performance problems and infrastructure improvements should be prioritised.\nEngineering Standards: Establish clear technical standards around inference performance, benchmarking, reproducibility, testing, and production readiness.\nTechnical Leadership: Guide and mentor engineers working across serving, metrics, and testing while remaining deeply involved in implementation and technical problem solving.\n05. REQUIREMENTS\nStrong hands-on experience building, optimising, or operating LLM inference-serving systems in production.\nDeep understanding of inference stacks such as vLLM, SGLang, TensorRT-LLM, or similar technologies.\nStrong understanding of paged attention, continuous batching, disaggregated serving, KV-cache management, and modern inference architectures.\nStrong GPU performance engineering experience using CUDA or Triton.\nStrong understanding of GPU performance characteristics including memory bandwidth, compute utilisation, model bandwidth utilisation, FLOPs utilisation, and modern GPU precisions such as FP8 and FP4.\nExperience serving large Mixture-of-Experts (MoE) models, including expert parallelism, all-to-all communication, and expert-load balancing.\nHands-on experience with model quantization and measuring quality against full-precision references.\nStrong benchmarking experience, including load generation, latency percentiles, throughput-at-SLO, quality measurement, and reproducibility.\nStrong understanding of distributed systems, particularly routing and scheduling workloads across varied hardware.\nAbility to analyse complex performance bottlenecks across models, serving infrastructure, networking, and hardware.\nDemonstrated technical leadership experience leading an inference, serving, ML infrastructure, or similarly complex engineering initiative.\nAbility to set technical direction while remaining hands-on with implementation and performance optimisation.\nExperience with speculative decoding and understanding its behaviour under production load is a strong plus.\nOpen-source contributions to inference or serving frameworks are a strong plus.\nExperience serving reasoning models, long-context models, or agentic multi-turn workloads is a plus.\nExperience with energy-aware serving, including throughput-per-watt or power-constrained environments, is a plus.\nExperience with confidential computing or data-residency-aware serving is a plus.\nProduction experience with model compression techniques such as structured sparsity, activation sparsity, or KV compression is a plus.\nEnglish level: Advanced (C1).\n06. WHAT YOU WILL LEARN\nInference at Scale: How to build and optimise production inference systems where latency, throughput, memory, model quality, hardware utilisation, and cost all need to be solved together.\nDistributed AI Infrastructure: Deep exposure to distributed inference across diverse hardware environments and the architectural challenges involved in scheduling, routing, caching, and serving large models efficiently.\nFrontier Serving Techniques: Hands-on work with emerging approaches across disaggregated serving, speculative decoding, quantization, MoE inference, model compression, and GPU optimisation.\nPerformance Engineering: How to build benchmarking and measurement systems that translate complex technical improvements into clear, reproducible performance and cost results.\nTechnical Leadership: The opportunity to define the serving architecture, engineering standards, and technical roadmap of a core FAR Labs product area.\n07. WHAT WE OFFER\nReal technical ownership of inference serving and performance across FAR Labs.\nThe opportunity to define the technical direction of a core part of the FAR Labs platform.\nHands-on work with technically challenging problems across LLM inference, GPU optimisation, distributed systems, and AI infrastructure.\nThe opportunity to work with large models and emerging inference techniques in a production environment.\nA small, senior engineering environment where technical decisions translate directly into product outcomes.\nFast execution, low bureaucracy, and a highly collaborative, idea-driven team.\nCompetitive salary with performance-based incentives.\n24 days annual leave, plus public holidays.\nHealth insurance.\nModern office in Yas Creative Hub.\nContinuous learning through real-world problem solving - not just theory.\nThe opportunity to influence both technical architecture and product direction.\nA diverse, open-minded team where ideas are genuinely heard.","description_format":"text","description_chars":8991,"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":[{"language":"English","level":"All levels","optional":false}]},"benefits":["Annual leave","Continuous learning","Health insurance"],"hiring_locations":[{"name":"United Arab Emirates","iso":"AE","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-14T05:41:15Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":26,"reasons":["conf:0","win:early"],"computed_at":"2026-10-14T06:34:16Z"},"pay":null,"html_url":"https://alion.io/job/farcana-lead-inference-engineer","json_url":"https://alion.io/job/farcana-lead-inference-engineer.json","meta":{"generated_at":"2026-10-14T19:45:04Z","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":26008,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-15T00:00:00Z"}},"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":4430105},"rest":"https://alion.io/mcp/rest/get_company?id=4430105"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ffarcana-lead-inference-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%2Ffarcana-lead-inference-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%2Ffarcana-lead-inference-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/farcana-lead-inference-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ffarcana-lead-inference-engineer"}]}