{"id":463181,"url":"https://alion.io/job/apollo-research-research-scientistengineer-evaluations","title":"Research Scientist/Engineer (Evaluations)","company":{"id":178413,"name":"Apollo Research","domain":"apolloresearch.ai","url":"https://alion.io/company/apollo-research","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"Data Science","role_family":"Data Science","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":{"min":100000,"max":200000,"currency":"GBP","period":"year","gross":null,"usd_annual":267715},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"OpenAI","optional":false},{"name":"Post-training","optional":false},{"name":"Knowledge Distillation","optional":true},{"name":"LLM","optional":true},{"name":"Model Distillation","optional":true},{"name":"Python","optional":true},{"name":"RLHF","optional":true},{"name":"SFT","optional":true}],"status":"closed","first_seen_at":"2026-02-13T16:54:24Z","employer_posted_date":"2026-02-13","last_verified_at":"2026-10-30T20:51:22Z","board_verified":false,"closed_at":"2026-10-30T20:51:22Z","days_open":259,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":257},"description":"Application deadline: We are conducting interviews actively and aim to fill this role as soon as we find someone suitable.\nABOUT THE OPPORTUNITY\nWe’re looking for Research Scientists/Engineers for our pre-deployment team to work on Training-Run Assessments (TRAs). You will design and build automated pipelines for assessing whether egregious misalignment or scheming are emerging at any point of frontier post-training.\nThis will involve evaluating and red-teaming of checkpoints at various stages of post-training as well as automated analysis of post-training data.\nYou will get to work with frontier labs like OpenAI, Anthropic, and Google DeepMind and be among the first to interact with new models before anyone else. Our ideal candidate loves rigorously testing frontier AI models, and enjoys building efficient pipelines for automated analysis.\nKEY RESPONSIBILITIES\nRun and own pre-deployment engagements: We run a pre-deployment evaluation campaign with a frontier AI lab approximately every two weeks with thousands of runs across hundreds of distinct environments. We explore behaviors learned during training and check for undesirable behaviors like alignment faking, perform targeted follow-up experiments/red-teaming, and report our findings to the frontier AI lab we’re working with.\nDevelop methodology for training-run assessments: in-between campaigns we improve our methodology, which might mean implementing new evals or building infrastructure for automated red-teaming.\nKEY REQUIREMENTS\nWe don’t require a formal background or industry experience and welcome self-taught candidates.\nSoftware engineering skills: Our entire stack uses Python. We're looking for candidates with strong software engineering experience. Ideally, you have experience shipping and maintaining production Python code, and know how to factor messy problems into clean abstractions that others can use and extend.\nData Analysis & Pattern Recognition: You can extract signal from large, messy datasets. You're comfortable with quantitative analysis and know when qualitative assessment is more appropriate. You can identify anomalies and unexpected model behaviors.\nWriting and communication: You succinctly convey qualitative and quantitative findings to a technical and non-technical audience.\nAI power-user: You’re capable of using AI to accelerate your work, technical or otherwise. You have experience using different models, know which ones to use for which tasks, when not to use AI, and always experiment with new AI workflows.\nNICE TO HAVE\nExperience thinking about AI risk topics like scheming and metagaming.\nKnowledge of LLM post-training: topics like RLHF, reasoning training, supervised fine-tuning, on-policy distillation, etc.\nWe are usingInspect as our primary evals framework, and we value experience creating evals with it or similar frameworks like Harbor.\nWe want to emphasize that people who feel they don’t fulfill all of these characteristics but think they would be a good fit for the position, nonetheless, are strongly encouraged to apply. We believe that excellent candidates can come from a variety of backgrounds and are excited to give you opportunities to shine.\nBENEFITS\n This role offers market competitive salary, equity, and competitive benefits.\nSalary: 100k - 200k GBP (~150k - 270k USD). We will be looking to meaningfully raise salaries soon. \nFlexible work hours and schedule\nUnlimited vacation\nUnlimited sick leave\nUp to 6 months of paid parental leave\nComprehensive health, dental and vision insurance\nRetirement savings with competitive employer matching (e.g. 401(k) for US employees)\nLunch, dinner, and snacks are provided for all employees on workdays\nPaid work trips, including staff retreats, business trips, and relevant conferences\nA yearly $1,000 (USD) professional development budget\nRelocation support and visa fees (if applicable)\nLOGISTICS\nTime Allocation: Full-time\nLocation: This is an in-person role working out of our London or San Francisco office. We offer flexible working hours and wfh arrangements. \n Visa sponsorship: We sponsor visas in both the UK and US. 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