{"id":126398,"url":"https://alion.io/job/kraken-senior-ai-compute-infrastructure-engineer","title":"Senior AI Compute Infrastructure Engineer","company":{"id":963,"name":"Kraken","domain":"kraken.com","url":"https://alion.io/company/kraken","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":87,"open_postings":6,"ghost_share":0,"stale_share":0.333,"repost_share":0.333,"time_to_fill_p50_days":44,"computed_at":"2026-10-26T05:57:25Z"}},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Panama"],"countries":["PA"],"hiring_countries":["GB","AR","BR","BG","CA","CY","CZ","HU","IE","MX","PL","RO","ZA","ES","CR","EE","LV","LT","PA","PE","PT","SI"],"hiring_countries_total":22,"salary":null,"salary_estimate":{"min_usd":83000,"max_usd":158000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":9},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"KServe","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Ray Serve","optional":false},{"name":"TensorRT","optional":false},{"name":"TorchServe","optional":false},{"name":"Triton Inference Server","optional":false},{"name":"vLLM","optional":false},{"name":"AWS","optional":true},{"name":"AWS Trainium","optional":true},{"name":"C++","optional":true},{"name":"CUDA","optional":true},{"name":"DeepSpeed","optional":true},{"name":"FSDP","optional":true},{"name":"Megatron-LM","optional":true},{"name":"NCCL","optional":true},{"name":"Rust","optional":true},{"name":"TPU","optional":true}],"status":"live","first_seen_at":"2026-05-07T11:57:34Z","employer_posted_date":"2026-05-07","last_verified_at":"2026-10-27T04:53:36Z","board_verified":true,"closed_at":null,"days_open":172,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":172},"description":"Building the Future of Open Finance\nPayward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system.\nThe team\nFounded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.\nKraken is building a dedicated AI Compute and Infrastructure team to power the next generation of model training, inference, evaluation, and experimentation across the exchange. This team sits within engineering leadership and owns the infrastructure layer that lets Kraken run AI workloads with control, speed, reliability, and cost discipline.\nThe team is responsible for GPU and accelerator infrastructure, cluster operations, scheduling, model serving, observability, capacity planning, and cost-efficient compute at scale. This is the backbone that allows Kraken to train, serve, evaluate, and iterate on AI systems in-house where it matters for privacy, latency, reliability, cost, or product differentiation.\nYou will join a small, senior, high-impact team working directly with AI/ML researchers, platform engineers, security teams, and product teams. The mandate is simple: make Kraken's AI ambitions real by building compute infrastructure that is fast, dependable, efficient, and production-grade.\nThe opportunity\nOwn and operate GPU and accelerator clusters used for training, inference, evaluation, and experimentation, including drivers, runtimes, kernels, device plugins, node configuration, scheduling primitives, and workload isolation.\n\nDesign infrastructure that enables Kraken teams to run models locally on GPUs where it is strategically and economically preferable, reducing unnecessary dependency on external providers and containing compute costs.\n\nBuild and improve scheduling, orchestration, placement, quota management, and utilization systems across heterogeneous accelerator environments.\n\nOptimize inference pipelines for latency, throughput, reliability, memory efficiency, and cost using frameworks such as vLLM, Triton Inference Server, TensorRT, or equivalent serving stacks.\n\nPartner with ML engineers and researchers to remove bottlenecks in training, evaluation, batch inference, online inference, deployment, and production debugging workflows.\n\nBuild observability for GPU utilization, memory pressure, queue depth, saturation, token throughput, request latency, failed workloads, capacity pressure, and spend.\n\nDrive reliability, incident response, alerting, runbooks, and post-incident improvements for always-on AI compute infrastructure.\n\nEvaluate and integrate new hardware, cloud instance families, specialized accelerators, runtimes, schedulers, and serving frameworks as the AI infrastructure landscape evolves.\n\nBuild tooling that makes GPU usage visible, accountable, and easier for internal teams to consume without needing to become infrastructure experts.\n\nContribute to long-term architecture decisions that balance performance, cost efficiency, scalability, operational simplicity, and production safety.\n\nWhat You Bring\n5+ years of infrastructure engineering experience, with significant time spent on GPU compute, ML infrastructure, distributed systems, high-performance computing, or large-scale production platforms.\n\nHands-on experience operating GPU clusters or accelerator-backed infrastructure in production or production-like environments, including scheduling, orchestration, utilization monitoring, and cost optimization.\n\nStrong systems engineering fundamentals across Linux, networking, storage, containers, Kubernetes, distributed runtimes, and production debugging.\n\nExperience with ML serving frameworks such as vLLM, Triton Inference Server, TensorRT, TorchServe, KServe, Ray Serve, or equivalent systems.\n\nProficiency in Python for infrastructure automation, tooling, debugging, integration, and operational workflows.\n\nPractical understanding of performance tradeoffs across batching, concurrency, memory usage, GPU utilization, model size, latency, throughput, availability, and cost.\n\nTrack record of optimizing compute costs while maintaining clear performance, reliability, and availability expectations.\n\nExperience building observable systems with useful metrics, logs, traces, dashboards, alerts, and incident workflows.\n\nComfortable working in high-stakes, always-on environments where uptime, throughput, correctness, and operational discipline are critical.\n\nClear communicator who can translate infrastructure tradeoffs for researchers, product teams, platform engineers, security stakeholders, and engineering leadership.\n\nNice to haves\nExperience at a frontier AI lab, hyperscaler, high-frequency trading firm, research platform, or high-scale ML organization.\n\nFamiliarity with custom silicon or specialized accelerators such as TPUs, AWS Trainium, Gaudi, or similar platforms.\n\nBackground in capacity planning, procurement input, reserved capacity strategy, cloud accelerator economics, or GPU fleet cost management.\n\nExperience with distributed training frameworks such as DeepSpeed, Megatron-LM, FSDP, Ray, or equivalent systems.\n\nExperience debugging CUDA, NCCL, kernel, driver, runtime, memory, networking, or low-level performance issues.\n\nExperience with Rust, C++, Go, CUDA, or other systems languages used for performance-critical infrastructure.\n\nCrypto, financial services, trading infrastructure, or security-sensitive production infrastructure experience.\n\nUnless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis.\nPlease note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution.\nWe consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.\nOur commitment\nPayward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto.\nWe may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision.\nAs an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws.\nStay connected\nFollow us on Twitter\nLearn on the Kraken Blog\nConnect on LinkedIn\n Candidate Privacy Notice","description_format":"text","description_chars":7533,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United Kingdom","iso":"GB","kind":"country"},{"name":"Argentina","iso":"AR","kind":"country"},{"name":"Brazil","iso":"BR","kind":"country"},{"name":"Bulgaria","iso":"BG","kind":"country"},{"name":"Canada","iso":"CA","kind":"country"},{"name":"Cyprus","iso":"CY","kind":"country"},{"name":"Czech Republic","iso":"CZ","kind":"country"},{"name":"Hungary","iso":"HU","kind":"country"},{"name":"Ireland","iso":"IE","kind":"country"},{"name":"Mexico","iso":"MX","kind":"country"},{"name":"Poland","iso":"PL","kind":"country"},{"name":"Romania","iso":"RO","kind":"country"},{"name":"South Africa","iso":"ZA","kind":"country"},{"name":"Spain","iso":"ES","kind":"country"},{"name":"Costa Rica","iso":"CR","kind":"country"},{"name":"Estonia","iso":"EE","kind":"country"},{"name":"Latvia","iso":"LV","kind":"country"},{"name":"Lithuania","iso":"LT","kind":"country"},{"name":"Panama","iso":"PA","kind":"country"},{"name":"Peru","iso":"PE","kind":"country"},{"name":"Portugal","iso":"PT","kind":"country"},{"name":"Slovenia","iso":"SI","kind":"country"},{"name":"Panama","iso":null,"kind":"city"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Blockchain & Crypto","Real Estate","Online Brokerage & Trading Platforms"],"lifecycle":[{"event":"open","at":"2026-05-07T11:57:34Z"}],"visa":[],"liveness":{"score":14,"band":"cold","label":"Long shot","p_open":1,"p_active":0.509,"p_room":0.28,"age_days":171,"expected_fill_days":44,"reasons":["conf:0","velocity","win:tail","crowd:"],"computed_at":"2026-10-26T05:57:25Z"},"pay":null,"html_url":"https://alion.io/job/kraken-senior-ai-compute-infrastructure-engineer","json_url":"https://alion.io/job/kraken-senior-ai-compute-infrastructure-engineer.json","meta":{"generated_at":"2026-10-27T05:23:58Z","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":5207,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-28T00:00:00Z"}},"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":963},"rest":"https://alion.io/mcp/rest/get_company?id=963"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fkraken-senior-ai-compute-infrastructure-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%2Fkraken-senior-ai-compute-infrastructure-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%2Fkraken-senior-ai-compute-infrastructure-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/kraken-senior-ai-compute-infrastructure-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fkraken-senior-ai-compute-infrastructure-engineer"}]}