{"id":1453617,"url":"https://alion.io/job/sash-research-engineer-neo","title":"Research Engineer - Neo","company":{"id":1896857,"name":"Singapore AI Safety Hub","domain":"aisafety.sg","url":"https://alion.io/company/aisafety-sg","size_band":"201-500","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":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":[],"countries":[],"hiring_countries":["SG"],"hiring_countries_total":1,"salary":{"min":120000,"max":180000,"currency":"USD","period":"year","gross":null,"usd_annual":180000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Python","optional":false},{"name":"Tool Use","optional":false}],"status":"live","first_seen_at":"2026-09-01T19:17:56Z","employer_posted_date":"2026-09-01","last_verified_at":"2026-10-11T21:11:57Z","board_verified":true,"closed_at":null,"days_open":40,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":40},"description":"About the team\nNeo Research (新衡) is an independent AI safety research organization based in Singapore. We study frontier risks in increasingly capable AI systems, with a particular focus on the open-weight model ecosystem and the rapidly growing frontier-model ecosystem in Asia.\nSome of the world’s most capable open-weight models are now being developed in Asia and deployed globally. Yet, they remain poorly understood from a frontier-safety perspective. We want to understand how to ensure their safety, and what new risks become important as the frontier changes.\nOur current work focuses on misalignment, loss of control, and harmful manipulation. We study questions such as whether models pursue unintended objectives, conceal problematic behaviour, recognize and adapt to evaluations, evade oversight, or become less safe as they are given greater autonomy. Our goal is to produce rigorous empirical evidence about risks that are important but difficult to measure.\nWe are looking for Research Engineers to build the experimental systems needed to evaluate increasingly capable models: realistic agent environments, model and tool integrations, long-horizon evaluation infrastructure, and the systems needed to make complex experiments reliable and reproducible.\nWhy join Neo Research\nEvaluating increasingly capable models requires more than running benchmarks. It means building realistic environments, giving models tools and autonomy, running experiments that may unfold over hundreds of interactions, and capturing enough information to distinguish genuine model behaviour from quirks or failures of the evaluation itself.\nYou will work closely with researchers while owning substantial parts of the experimental system,from agent scaffolds and tool integrations to model sampling, observability, trajectory analysis, and reproducibility. In this role, you will go beyond implementing evaluations and actively help shape evaluation methodology.\nYour work will contribute directly to published research shared with AI Safety Institutes, frontier labs, policymakers, and model developers. We have presented our work to most major Chinese model developers and run a joint evaluations project with an AI Safety Institute. Ourmission statement describes our current research directions.\nIn this role, you would:\nDesign, implement, and run evaluations of misalignment and loss-of-control risks in frontier models.\n\nBuild realistic agent environments, scaffolds, and tool integrations for studying behaviour under increasing autonomy.\n\nDevelop infrastructure for long-horizon experiments involving many model calls, actions, tools, and environment states.\n\nOwn experimental reliability and reproducibility across model access, sampling, configuration, environment management, logging, and analysis.\n\nWork with research scientists to turn open-ended questions into tractable experiments, and identify cases where an apparent model behaviour is actually an artefact of the evaluation.\n\nBuild tools for inspecting and analysing large collections of model trajectories and comparing behaviour across models and experimental conditions.\n\nContribute experimental methodology, infrastructure, and technical analysis directly to published research.\n\nAbout you\nEssentials\nStrong Python and general software-engineering skills.\n\nExperience working with language models, agentic systems, or model evaluations.\n\nThe ability to turn an underspecified research question into a reliable experimental setup.\n\nStrong engineering practices, particularly around reproducibility, testing, observability, and data integrity.\n\nThe ability to investigate unexpected results across both the model and the surrounding infrastructure.\n\nClear technical writing and communication skills.\n\nComfort working in an early-stage environment where requirements and research directions evolve.\n\nHelpful, but not required\nExperience with AI safety or dangerous-capability evaluations.\n\nExperience with evaluation frameworks such as Inspect.\n\nExperience building agent scaffolds or tool-use environments.\n\nExperience with distributed inference, large-scale API-based experimentation, Docker, Kubernetes, or related infrastructure.\n\nExperience analysing large collections of model trajectories or transcripts.\n\nFamiliarity with frontier-model safety reports.\n\nMandarin reading or writing ability.\n\nYou don't need to have worked in AI safety specifically. We're also interested in strong ML and research engineers whose technical expertise and engineering judgment could transfer strongly to this work.\nRole logistics & benefits\nLocation:\nThis role can be based in Singapore or remotely.\nOur team is globally distributed, so remote team members should be comfortable maintaining some working-hour overlap with colleagues across regions.\n\nCompensation:\nOur compensation takes location, experience, and level into account, with indicative salary ranges of $120,000-$180,000+. We may offer above this range for exceptional candidates.\nBenefits:\nCompetitive benefits and leave policies.","description_format":"text","description_chars":5048,"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":[],"hiring_locations":[{"name":"Singapore","iso":"SG","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Cybersecurity AI","AI Safety & Alignment"],"lifecycle":[{"event":"open","at":"2026-09-29T08:47:08Z"}],"visa":[],"liveness":{"score":26,"band":"fade","label":"Fading","p_open":1,"p_active":0.58,"p_room":0.45,"age_days":38,"expected_fill_days":24,"reasons":["conf:1","velocity","win:tail"],"computed_at":"2026-10-10T05:45:15Z"},"pay":{"stated_usd_annual":180000,"is_top_pay":false},"html_url":"https://alion.io/job/sash-research-engineer-neo","json_url":"https://alion.io/job/sash-research-engineer-neo.json","meta":{"generated_at":"2026-10-11T23:04:28Z","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":13537,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-12T00:00:00Z"}},"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":1896857},"rest":"https://alion.io/mcp/rest/get_company?id=1896857"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsash-research-engineer-neo"},{"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%2Fsash-research-engineer-neo"},{"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%2Fsash-research-engineer-neo"},{"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/sash-research-engineer-neo\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsash-research-engineer-neo"}]}