{"id":619980,"url":"https://alion.io/job/ema-ai-resident","title":"AI Resident","company":{"id":26744,"name":"Ema","domain":"ema.ai","url":"https://alion.io/company/ema","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":79,"open_postings":38,"ghost_share":0.263,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":142,"computed_at":"2026-10-16T05:45:00Z"}},"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":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":196000,"max_usd":429000,"period":"year","method":"role_country_seniority_unknown","sample_n":4027},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Context Engineering","optional":false},{"name":"DPO","optional":false},{"name":"GRPO","optional":false},{"name":"LLM","optional":false},{"name":"Okta","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reward Modeling","optional":false},{"name":"SFT","optional":false},{"name":"Tool Use","optional":false},{"name":"Knowledge Distillation","optional":true},{"name":"Model Distillation","optional":true},{"name":"Quantization","optional":true},{"name":"SGLang","optional":true},{"name":"Synthetic Data","optional":true},{"name":"Transformers","optional":true},{"name":"TRL","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-07-31T23:26:12Z","employer_posted_date":"2026-07-31","last_verified_at":"2026-10-16T18:10:01Z","board_verified":true,"closed_at":null,"days_open":76,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":76},"description":"About Ema\nEma builds AI Employees for HR, IT and Finance. Our AI Employees take on the busy work across the employee experience, from recruiting, onboarding and benefits to IT support, invoice processing and payroll, so people can spend their time on work that needs them. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs.\nWe are backed by industry leading investors including Accel, Naspers/Prosus, Creaegis, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production. We ship real systems that run real business processes at scale.\nThe residency\nYou own one hard problem end to end. You write the proposal, build the system, design the evaluation, ship behind a gate, and finish with a write-up of what turned out to be true, including the parts that didn't work. You'll sit in the production codebase with a senior mentor and real production data. Recent residents have shipped self-improving harnesses, inference-cost work, agent memory, and eval infrastructure. Your project gets scoped with you, not handed to you.\nThe problem space\nThe loop we care about: production traces become data, data becomes training and evaluation, and better agents produce better traces. Projects live somewhere on that loop.\nHarness and inference-time work. Context engineering, tool and skill design, orchestration, and deciding where extra inference compute actually pays. Self-improvement loops run behind hard fences.\n\nPost-training for agents. SFT on curated trajectories, preference optimization, RL on real agent tasks. Reward design where outcomes are verifiable, process vs. outcome supervision, distilling frontier behavior into cheaper models.\n\nEnvironments and rewards. Turning enterprise workflows into training and eval environments: fixture tenants, simulated users (some of whom get impatient and leave), verifiable rewards, and defenses against reward hacking. Agents will exploit a lazy grader.\n\nData engines. Mining production agent-steps into training and eval corpora: failure mining, labeling with calibrated judges, synthetic augmentation that stays useful.\n\nEvaluation. Behavior-level benchmarks from real workflows, LLM judges calibrated against human labels, reliability statistics for stochastic agents.\n\nEfficiency. Routing, ensembles, caching, small-model specialization. Quality per dollar is a research metric here.\n\nWhat we're looking for\nNo specific degree required. Strong undergrads, grad students, and self-taught builders are all welcome; what matters is demonstrated depth in ML or agent systems.\n\nSolid ML fundamentals and strong engineering: Python, PyTorch, and the discipline to ship in a large production codebase.\n\nReal depth in at least one of: post-training (SFT/DPO/GRPO-family RL), reward modeling or LLM judges, agent and tool-use systems, retrieval and memory, eval design. One area you can teach us beats five you've touched.\n\nStatistical literacy. You can size an experiment, and you know 25 samples at one seed is a datapoint, not a result.\n\nHonest measurement as a habit. You'd rather kill your own feature with a clean experiment than ship it on a hunch.\n\nNice to have\nHands-on post-training with open models (TRL, veRL, OpenRLHF, or your own loop). Bonus points if you've debugged a reward-hacked run.\n\nBuilt or trained in interactive agent environments (SWE, web, or tool-use gyms).\n\nLarge-scale trace analysis, data curation, or synthetic data work.\n\nServing and efficiency experience: vLLM/SGLang, distillation, quantization.\n\nMulti-node GPU training, or the infra fluency to get there fast.\n\nPublications, open-source work, or writing that shows how you think.\n\nSecurity instincts: prompt injection, data governance, why a self-improving agent needs a fence.\n\nLogistics\nSF Bay Area, on-site/hybrid, half/full-time for the term. Flexible start.\n\nSalary: $4,000 month\n\nCompensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.\nEma Unlimited is an equal opportunity employer and is committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or genetics.","description_format":"text","description_chars":4591,"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"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["LLM & Generative AI","AI Agents"],"lifecycle":[{"event":"open","at":"2026-09-10T18:43:48Z"}],"visa":[],"liveness":{"score":61,"band":"ok","label":"Likely open","p_open":1,"p_active":0.846,"p_room":0.72,"age_days":76,"expected_fill_days":142,"reasons":["conf:1","velocity","win:mid","crowd:"],"computed_at":"2026-10-16T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/ema-ai-resident","json_url":"https://alion.io/job/ema-ai-resident.json","meta":{"generated_at":"2026-10-16T19:32:01Z","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":5190,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"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":26744},"rest":"https://alion.io/mcp/rest/get_company?id=26744"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fema-ai-resident"},{"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%2Fema-ai-resident"},{"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%2Fema-ai-resident"},{"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/ema-ai-resident\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fema-ai-resident"}]}