{"id":2634800,"url":"https://alion.io/job/cosine-machine-learning-engineer-lumen-enterprise-models-swe-focused-llms","title":"Machine Learning Engineer – Lumen Enterprise Models (SWE-focused LLMs)","company":{"id":177875,"name":"Cosine","domain":"cosine.sh","url":"https://alion.io/company/cosine-2","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"DeepSpeed","optional":false},{"name":"FSDP","optional":false},{"name":"Machine Learning","optional":false},{"name":"Pre-training","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"GDPR","optional":true},{"name":"Kubernetes","optional":true},{"name":"Mixture of Experts","optional":true},{"name":"SGLang","optional":true},{"name":"Slack","optional":true},{"name":"SLURM","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-15T12:54:36Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-19T18:50:54Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"Job title: Machine Learning Engineer - Lumen Enterprise Models (SWE-focused LLMs)\nLocation: London; full in-office working as default\nStart date: ASAP\nReports to: CEO\nCompensation: Salary and Equity options available for discussion\nApplication deadline: 08/11/26 at 23:59 UK time\n___________________________________________________________________________\nHelp train the software engineers of the future\nCosine is building autonomous software-engineering systems for organisations where reliability, security and control matter most.\nOur Lumen Enterprise models are designed to work across real codebases. They need to understand unfamiliar architectures, make changes across multiple files, use tools, run tests and produce software that engineers can maintain and trust.\nWe’re looking for a Machine Learning Engineer to help train and improve these models.\nYou’ll work across the full training stack, including pre-training, supervised fine-tuning and reinforcement learning. This is a truly unique role that will provide exposure across all areas of the model training lifecycle. WE’re looking for someone who is just as happy to profile the training loop, identifying performance bottlenecks and memory leaks, as implementing new RL algorithms.\nThis is a hands-on role for someone who enjoys working close to the model and cares about the quality of the software it produces.\n___________________________________________________________________________\nThe role\nYou’ll work closely with research, infrastructure and product to answer questions such as:\nWhat should Lumen learn next?\n\nWhich data and environments will teach it most effectively?\n\nHow should we reward correct, maintainable and useful software?\n\nHow do we train models to work across large repositories and long contexts?\n\nHow do we know when a model is genuinely better for engineers?\n\nThe models you train will write code, use development tools, run tests and receive feedback from real software-engineering environments. Your work will directly influence how Lumen reasons about code, navigates unfamiliar systems and improves through experience.\n___________________________________________________________________________\nWhy this role matters\nThe best coding models are not simply the ones that generate plausible code.\nThey need to understand context, respect existing architecture, use tools effectively, test their work and recover when their first attempt fails. They need to produce changes that work in the real world, not just on a benchmark.\nThat behaviour has to be trained.\nYou’ll work directly on the data, environments, rewards and evaluations that shape Lumen’s capabilities. Your work will influence the models used by engineers and enterprise teams working on complex and security-sensitive software systems.\nThis is an opportunity to work on frontier model training while staying close to a concrete, high-value outcome: building AI that can genuinely do software engineering.\n___________________________________________________________________________\nWhat you’ll do\nWork across the full training lifecycle, running pre-training, supervised fine-tuning and reinforcement-learning experiments.\n\nOptimize training methods at scale on frontier models.\n\nDiagnose and fix performance bottlenecks and communication overhead in distributed training environments.\n\nTrain models on long-context, multi-file and repository-level tasks.\n\nDevelop RL and RLVR loops using signals from execution, tests, linters, static analysis and other engineering feedback, turning fresh ideas into scalable, reproducible code.\n\nInvestigate model failure modes such as poor planning, brittle fixes, reward hacking and regressions.\n\nCollaborate with infrastructure, platform and data teams.\n\n___________________________________________________________________________\nWhat we’re looking for\nYou may come from machine learning research, applied ML, model training or a closely related engineering background.\nYou should have:\nStrong experience training deep-learning models in a production or research environment.\n\nDeep practical experience with PyTorch or a comparable framework.\n\nExperience with distributed training and multi-GPU systems (FSDP, Deepspeed, …)\n\nStrong software-engineering fundamentals and a high bar for correctness and maintainability.\n\nClear communication and the ability to explain technical trade-offs.\n\nExperience with large language models is important. You should have worked meaningfully on at least one of, pre-training, SFT, preference optimisation, RLHF or RLVR.\n\nWe care more about depth of understanding and evidence of strong technical judgement than about having used every tool listed. Even if you don’t tick every box, please consider applying!\nA bonus if you have\nExperience with mixture-of-experts (MoE) architectures.\n\nExperience with CUDA.\n\nFamiliarity with vLLM, SGLang or other high-performance inference systems.\n\nExperience running jobs on SLURM, Kubernetes or managed GPU platforms.\n\nExperience with code-model benchmarks such as SWE-bench, HumanEval or internal repository-level evaluations.\n\nContributions to open-source model-training, RL or evaluation tools.\n\n___________________________________________________________________________\nWhy join Cosine\nDirect impact: Your work directly shapes the next generations of Lumen Enterprise SWE models that engineers use every day.\n\nReal scale: You’ll work with large, modern open-source models, long context lengths, and multi-node training runs.\n\nFull-stack ML engineering: From custom PyTorch code and distributed systems to data curation, RL design and MLOps.\n\nResearch + pragmatism: You’ll stay close to the latest literature in SFT, and code LLMs, but you’ll be judged by shipped improvements, not just ideas.\n\nIf this sounds like a fit, this is a role where you can meaningfully push the frontier of open-source-based software engineering models.\n___________________________________________________________________________\n Cosine is an equal opportunity employer.\nWe value diverse backgrounds, perspectives, and ways of thinking, and we’re committed to creating an inclusive and respectful workplace.\nWe encourage applications from anyone who meets the role requirements, even if you don’t meet every single qualification. If you need reasonable adjustments at any stage of the hiring process, we’re happy to discuss them.\n___________________________________________________________________________\nCompensation, Benefits & Ways of Working\nWe’re an in-office team, five days a week, by design. We believe the work we’re doing benefits from being together, collaborating closely, and building shared context.\nWhat you can expect:\nCompetitive salary, benchmarked to the market\n\nEquity / share options, so you share in the upside you help create\n\n30 days’ holiday + bank holidays\n\nWork hard in the office, collaborate closely, and switch off properly\n\nDog-friendly office - bring your dog to work\n\nWeekly team breakfast & lunch\n\nMonthly socials\n\nPension\n\nHigh-quality equipment to do your best work\n\nWe care about focus, sustainability, and doing great work - not performative overwork. We value people who show up, contribute thoughtfully, collaborate well with their colleagues, and then go home.\nThis role won’t suit everyone. But if you’re looking for a fast-moving environment, close collaboration, and a team that takes both the work and work-life balance seriously, it’s a great place to be.\n___________________________________________________________________________\nAgency & Data Protection Notice\nTo comply with UK GDPR and our internal data-protection and equal-opportunity obligations, we only accept candidate applications and agency submissions via our Applicant Tracking System (ATS). This ensures appropriate privacy notices, lawful processing, auditability, and consistent retention controls.\nAny CVs or candidate details received outside the ATS (including via email, Slack, or direct message) will be treated as unsolicited, will not be considered as part of the recruitment process, and will not give rise to any fee or payment obligation.","description_format":"text","description_chars":8117,"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":["Equity","Stock options"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Agents","Code Intelligence"],"lifecycle":[{"event":"open","at":"2026-10-15T17:10:16Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":3,"expected_fill_days":32,"reasons":["conf:2","velocity","win:early"],"computed_at":"2026-10-19T05:45:15Z"},"pay":null,"html_url":"https://alion.io/job/cosine-machine-learning-engineer-lumen-enterprise-models-swe-focused-llms","json_url":"https://alion.io/job/cosine-machine-learning-engineer-lumen-enterprise-models-swe-focused-llms.json","meta":{"generated_at":"2026-10-19T20:19:09Z","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":16136,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-20T00:00:00Z"}},"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":177875},"rest":"https://alion.io/mcp/rest/get_company?id=177875"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fcosine-machine-learning-engineer-lumen-enterprise-models-swe-focused-llms"},{"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%2Fcosine-machine-learning-engineer-lumen-enterprise-models-swe-focused-llms"},{"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%2Fcosine-machine-learning-engineer-lumen-enterprise-models-swe-focused-llms"},{"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/cosine-machine-learning-engineer-lumen-enterprise-models-swe-focused-llms\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fcosine-machine-learning-engineer-lumen-enterprise-models-swe-focused-llms"}]}