{"id":720983,"url":"https://alion.io/job/whitecircle-research-engineer-evals","title":"Research Engineer (Evals)","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":"2026-11-01T06:10:25Z"}},"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":57000,"max_usd":123000,"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":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"Synthetic Data","optional":false},{"name":"Anthropic","optional":true},{"name":"Datadog","optional":true},{"name":"Hugging Face","optional":true},{"name":"Mistral","optional":true},{"name":"OpenAI","optional":true},{"name":"Sentry","optional":true}],"status":"live","first_seen_at":"2026-06-28T13:19:33Z","employer_posted_date":"2026-06-28","last_verified_at":"2026-11-01T12:13:43Z","board_verified":true,"closed_at":null,"days_open":126,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":125},"description":"TL;DR: We're looking for a research engineer to build and maintain our internal suite of benchmarks, covering single/multi-turn content and agentic guardrails. The research engineer would also work with the team on projects studying agent behaviours in the wild.\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.\nAbout the team\nWhite Circle's fundamental research team works on the science of how AI systems fail in the real world: where agents break, how misalignment actually looks like to the end user, and how it is present in the model internals. We build the evals, benchmarks, environments, and tooling that empirically study high-impact agent reliability concerns - some of which become the guardrails shipped in our products, and some of which become public writeups.\nWhat you’ll do\nOwn and maintain our internal benchmark suite, covering single/multi-turn content guardrails and agentic safety.\nBuild benchmarks that distinguish specific model capabilities.\n\nWork with the product team to build evals covering core functionality of our flagship models.\n\nBuild benchmarks for new features coming out of the research team.\n\nAdapt and extend evals to new verticals and changing product data.\n\nWork on research projects that study and quantify realistic agentic and LLM failure modes in the wild.\n\nYou’ll fit right in if you\nHave built an LLM benchmark from scratch that distinguished specific model capabilities (i.e., produced a measurable, defensible capability difference, not just a score).\n\nHave built synthetic data for post-training textual or multimodal models.\n\nCan reproduce a published benchmark result and identify where the original methodology is fragile or misleading.\n\nYou write Python that other people can build on. Our whole stack is Python; we want someone who has shipped and maintained production code and who factors messy problems into clean abstractions others can extend.\n\nYou can write efficient LLM inference setups, including sensible orchestration of parallel calls, retries, rate-limit handling.\n\nAn AI power-user - fluent with frontier models and coding agents day to day.\n\nA big plus\nAutomated red-teaming experience\n\nHave worked across a range of agentic scaffolds and reproduced public benchmark results on them\n\nStrong knowledge of existing reward-model / monitoring / safety benchmarks\n\nOne or more published papers in the evals / safety-evaluation space\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\n\nTest assignment\n\nTechnical interview with Head of Fundamental Research\n\nFinal interview with CEO","description_format":"text","description_chars":4025,"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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