{"id":771190,"url":"https://alion.io/job/pinely-senior-research-engineer-ml-infrastructure-","title":"Senior Research Engineer (ML infrastructure )","company":{"id":686734,"name":"Pinely","domain":"pinely.com","url":"https://alion.io/company/pinely","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"C","score":68,"open_postings":17,"ghost_share":0.529,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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"],"countries":["NL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":70000,"max_usd":138000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":21},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[],"status":"live","first_seen_at":"2026-10-12T18:13:07Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-13T01:30:44Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":41},"description":"We are looking for a Senior Research Engineer to build and improve the ML infrastructure that powers our deep learning research and trading.\nOur DL strategies are becoming an increasingly important driver of the business, and the infrastructure behind them has a direct impact on how quickly researchers can test ideas, how efficiently we use compute, and how reliably successful research reaches production across markets and asset classes.\nYou will work at the boundary between deep learning research and infrastructure. Your job is not just to maintain ML systems, but to identify what is slowing research down, solve technically difficult problems across the stack, and turn research prototypes into reliable, scalable production systems.\nThe scope is broad: training frameworks, datasets and data pipelines, distributed training, experiment infrastructure, reproducibility, observability, and tooling around the research lifecycle. Depending on your strengths, you may go deep in one of these areas or work across several of them.\nThis is a highly hands-on IC role with substantial technical ownership. You will work closely with researchers, other research engineers, and infrastructure teams, and will be expected to independently drive important problems from diagnosis to production.\nResponsibilities\nTurn successful research prototypes into robust implementations that can be trained, validated, and deployed across multiple markets and asset classes;\nWork directly with researchers to remove infrastructure bottlenecks from the research loop - make new ideas easier to prototype, experiments faster to run, and failures easier to understand;\nBuild and improve our internal ML platform - training framework, datasets and data pipelines, orchestration, experiment tracking and reproducibility tooling, GPU/compute infrastructure, and tooling for releasing models into production;\nOwn technically challenging areas of the ML stack end-to-end: identify problems, design solutions, implement them, measure their impact, and maintain them in production;\nMake training reproducible and observable: investigate regressions, debug models that no longer reproduce from master, and improve tooling around data, experiments, model quality, and training behavior;\nTake ownership of shared training, dataset, and research infrastructure code - proactively find bugs, reduce technical debt, improve abstractions, and maintain a high bar through rigorous code review;\nWork across team boundaries when a research problem spans datasets, storage, compute, training infrastructure, or production systems;\nIdentify areas where researchers are repeatedly paying an infrastructure tax and build reusable solutions instead of fixing the same problem case by case;\nWhen useful, contribute directly to research: run experiments, investigate model behavior, prototype architectural or optimization ideas, and help push model quality forward.\nRequirements\nA strong, versatile ML systems / research engineer who is comfortable working on ambiguous problems at the intersection of deep learning and infrastructure;\nStrong background in at least one of:\nlarge-scale ML training systems and distributed training;\nML/data infrastructure, including datasets, storage formats, orchestration, experiment tracking, and configuration;\nresearch engineering around large-scale deep learning systems;\nHands-on experience making ML research faster or more reliable: for example, by reducing training time, improving hardware utilization, accelerating data access, improving experimentation workflows, or eliminating recurring infrastructure bottlenecks;\nAbility to independently investigate complex systems: profile them, form hypotheses, run experiments, trace problems across abstraction boundaries, and arrive at practical solutions;\nStrong engineering judgment and a high bar for code quality, reproducibility, observability, and maintainability in a large shared ML codebase;\nStrong product sense toward internal research infrastructure: you care not only whether a system works, but whether researchers can use it effectively and iterate quickly;\nAbility to work closely with researchers, understand what they are trying to achieve, and translate research needs into infrastructure improvements that compound over time;\nStrong ownership and initiative: you notice important problems that do not have a clear owner and are willing to take them from an unclear state to a working solution;\nAbility to influence technical direction through expertise, design work, code review, and collaboration without requiring formal management authority;\nWhile prior trading experience can be useful, it is not a prerequisite. Our priority is a first-principles mindset, strong technical depth, and a willingness to rethink how quantitative ML research infrastructure should work.\nWhat we offer\nA dynamic and international environment within a fast-growing tech company;\nSalary at the upper end of the market, along with significant bonuses twice a year;\nFreedom to shape internal culture and create meaningful employee experiences, team-building activities;\nHealth insurance, reimbursement of sports expenses; coverage for language courses;\nA supportive, experienced team that values collaboration - and where your impact will be visible every day.","description_format":"text","description_chars":5309,"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":["Health insurance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Online Brokerage & Trading Platforms"],"lifecycle":[{"event":"open","at":"2026-09-11T21:41:38Z"}],"visa":[],"liveness":{"score":18,"band":"cold","label":"Long shot","p_open":1,"p_active":0.404,"p_room":0.45,"age_days":41,"expected_fill_days":22,"reasons":["conf:1","stale_co","win:tail"],"computed_at":"2026-10-12T06:55:09Z"},"pay":null,"html_url":"https://alion.io/job/pinely-senior-research-engineer-ml-infrastructure-","json_url":"https://alion.io/job/pinely-senior-research-engineer-ml-infrastructure-.json","meta":{"generated_at":"2026-10-13T02:51:45Z","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":4439,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-14T00:00:00Z"}},"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":686734},"rest":"https://alion.io/mcp/rest/get_company?id=686734"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fpinely-senior-research-engineer-ml-infrastructure-"},{"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%2Fpinely-senior-research-engineer-ml-infrastructure-"},{"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%2Fpinely-senior-research-engineer-ml-infrastructure-"},{"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/pinely-senior-research-engineer-ml-infrastructure-\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fpinely-senior-research-engineer-ml-infrastructure-"}]}