{"id":720977,"url":"https://alion.io/job/whitecircle-ml-research-engineer","title":"ML Research Engineer","company":{"id":720703,"name":"Whitecircle","domain":"whitecircle.com","url":"https://alion.io/company/whitecircle-2","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":93,"open_postings":13,"ghost_share":0,"stale_share":0.077,"repost_share":0,"time_to_fill_p50_days":107,"computed_at":"2026-11-04T06:34:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Paris, France","London, United Kingdom"],"countries":["FR","GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":55000,"max_usd":118000,"period":"year","method":"role_country_seniority_unknown","sample_n":121},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DPO","optional":false},{"name":"Embeddings","optional":false},{"name":"GitHub","optional":false},{"name":"GRPO","optional":false},{"name":"Hugging Face","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"Reward Modeling","optional":false},{"name":"RLHF","optional":false},{"name":"Semantic Search","optional":false},{"name":"SFT","optional":false},{"name":"SQL","optional":false},{"name":"Anthropic","optional":true},{"name":"Datadog","optional":true},{"name":"OpenAI","optional":true},{"name":"Sentry","optional":true}],"status":"live","first_seen_at":"2026-07-02T13:58:22Z","employer_posted_date":"2026-07-02","last_verified_at":"2026-11-04T16:05:20Z","board_verified":true,"closed_at":null,"days_open":125,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":124},"description":"TL;DR: We are looking for several ML Engineers to train, post-train, and evaluate the LLMs at the core of our platform. This is hands-on modern model training work: large-scale data pipelines, SFT/RLHF/DPO-style alignment, reward models, distributed multi-GPU training, and evaluation.\nAbout us\nWhite Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies - simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale.\nWe’ve recently raised our Series A funding round, taking our total funding to $70M. Our investors include top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and others\n\nWe process over 100M+ API calls every month\n\nWe fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model\n\nWe’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built - you’re the one we need.\nWhat you’ll do\nTurn petabytes of unstructured text into a structured, explorable view (topics, clusters, segments, trends, anomalies): iterate from “unknown unknowns” to stable definitions we can track.\n\nBuild scalable representation pipelines: sampling strategies, preprocessing/normalization, embeddings at scale, indexing, and retrieval to make the corpus searchable and analyzable.\n\nUse LLMs pragmatically: labeling/classification, weak supervision, data enrichment, summarization, and automated diagnostics of inbound volumes (with cost/quality controls).\n\nDeliver insights that change decisions: translate findings into product and operational actions (what data we have, what’s missing, where quality breaks, what to prioritize next).\n\nShip self-serve analytics: datasets, data models, and lightweight tools/dashboards so the team can explore and answer questions without ad-hoc requests.\n\nPartner closely with engineering/research: align pipelines with production constraints (latency/cost/privacy), and integrate outputs into workflows.\n\nYou'll fit right in if you\nHave strong Python + SQL with an engineering mindset: you can build reliable pipelines, not just notebooks.\n\nHave solid applied NLP/ML experience on real-world text: embeddings, clustering, topic modeling, semantic search, classification; you understand failure modes and how to debug them.\n\nAre comfortable at scale: distributed processing, large-scale storage-querying, and performance-cost tradeoffs.\n\nKnow how to evaluate fuzzy problems: offline/online metrics, human-in-the-loop labelling, inter-annotator agreement, drift monitoring, and reproducibility.\n\nHave prior work with safety/moderation datasets, policy/rule systems, or high-volume logging/observability\n\nA big plus\nA public builder footprint: open-source models, datasets, or training frameworks on HuggingFace/GitHub, benchmarks, papers (workshop or main conference), or technical posts with real usage\n\nExperience training models at a frontier or near-frontier lab, or leading open-source model releases with documented adoption\n\nExperience with RL methods for LLMs beyond standard RLHF: online RL, GRPO-style methods, or novel alignment approaches\n\nExperience with moderation, safety, or classification models at scale\n\nMultilingual model training experience\n\nCompensation & benefits\nCompetitive compensation, including equity\n\nFlexible time off\n\nOffice in central London/Paris with flexible hybrid setup\n\nRelocation support if you’re moving to Paris, available after your probationary period\n\nPremium private health insurance\n\nMental health support, including coverage for therapy when you need it\n\nLunch and dinner covered when you work from the office\n\nLearning and development support for courses, conferences, and opportunities to grow your skills\n\nAll the hardware, subscriptions, tools, and services you need\n\nTeam off-sites twice a year: we’ve recently been to the Alps, Saint-Tropez, and Marbella\n\nProcess\nIntro call with Talent Team\n\nTest assignment\n\nTechnical interview with Head of Applied Research\n\nFinal conversation with CEO","description_format":"text","description_chars":4268,"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","Flexible time off","Health insurance"],"hiring_locations":[{"name":"France","iso":"FR","kind":"country"},{"name":"United Kingdom","iso":"GB","kind":"country"}],"hiring_excludes":[],"relocation_offered":true,"industries":["AI Safety & 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