{"id":1786443,"url":"https://alion.io/job/echelon-ai-inc-ai-research-engineer","title":"AI Research Engineer","company":{"id":2250693,"name":"Echelon AI Inc","domain":"echelonai.com","url":"https://alion.io/company/echelonai","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Kula","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":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":134000,"max_usd":297000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"DPO","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GDPR","optional":false},{"name":"ISO 27001","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"OpenAI","optional":false},{"name":"Post-training","optional":false},{"name":"PPO","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLAIF","optional":false},{"name":"ServiceNow","optional":false},{"name":"SOC 2","optional":false},{"name":"Function Calling","optional":true},{"name":"ITSM","optional":true},{"name":"LLM","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-10-02T17:27:21Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-11-15T09:37:13Z","board_verified":true,"closed_at":null,"days_open":44,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":44},"description":"AI Research Engineer\nEchelon AI · Full-time · In-person - San Francisco\nWho we are\nAbout Echelon\nEchelon is an AI delivery platform for ServiceNow. We build AI agents that plan, implement, and validate ServiceNow solutions, compressing weeks of consultant effort into hours without sacrificing quality. Fortune 500 companies and the ServiceNow partners who serve them already use Echelon to clear their backlogs: one major CRM provider unlocked 4,000 developer hours and cleared more than 1,000 tickets, a Fortune 10 healthcare company took a CSM migration from eight months down to ten weeks, and a large insurance provider replaced a year-plus migration with a ten-week ITSM go-live, executed correctly the first time.\nEchelon was founded by the AI leadership team behind Moveworks, which was acquired by ServiceNow for $3 billion, and is backed by leading investors and executives from ServiceNow, OpenAI, Meta, and Salesforce. We're a ServiceNow Registered Partner for both Consulting & Implementation and Build, and we're SOC 2, ISO 27001, and GDPR compliant.\nThe role\nPart scientist, part systems engineer. We're looking for a research engineer who will research and build long-horizon AI agents that perform end-to-end enterprise change delivery, from planning through testing and deployment, safely and autonomously. You'll build the foundation of autonomous enterprise AI: the reasoning, learning, and safety core that makes digital labor real.\nThe challenge: foundation models can describe changes, but they can't yet own them. You'll invent new reinforcement learning, planning, and evaluation methods that enable AI agents to handle real enterprise complexity, including multi-step reasoning, compliance guardrails, and measurable outcomes. Your work generalizes across thousands of customer workflows, and your fingerprints will be on how autonomous agents deliver enterprise change, not just at Echelon but across the industry.\nWhat you'll do\nBuild RL-driven agent architectures that plan, execute, and validate enterprise platform changes across ServiceNow, SAP, and NetSuite\n\nDesign hierarchical planners that decompose ambiguous business requests into executable workflows\n\nDevelop multi-agent coordination between planner, executor, and verifier roles with shared scratchpads and safety guardrails\n\nImplement post-training and fine-tuning pipelines across heterogeneous enterprise data such as schemas, ACLs, workflows, and integration patterns\n\nBuild RL and preference optimization loops (offline RL, DPO, PPO, RLAIF) that target business outcomes, not token accuracy\n\nCreate simulation sandboxes and gym environments that safely evaluate agents pre-deployment\n\nAuthor eval suites that replicate real customer requests, regression tests, and compliance scenarios\n\nBuild metrics that connect agent decisions to measurable business KPIs and surface attribution automatically\n\nEngineer reliability via deterministic replays, rollback policies, and isolation boundaries for PII and compliance\n\nBuild model inference and serving pipelines that optimize compute across agent roles\n\nPartner with applied engineers and product to deploy learning loops to Fortune 500 environments\n\nDocument, publish internally, and set standards for reproducibility, red-teaming, and experiment hygiene\n\nWho you are\nWe're looking for someone who meets the minimum requirements below. If you meet them, you're encouraged to apply. The preferred qualifications are a bonus, not a requirement.\nMinimum requirements\nResearch engineer mindset, blending deep ML/RL intuition with strong software engineering fundamentals\n\nA builder who has shipped ML or LLM systems that work in production, not just papers or prototypes\n\nA curious experimentalist who instruments, tests, and iterates until it works safely and reliably\n\nA systems thinker who sees the whole stack: model, API, sandbox, deployment, and metrics\n\nOutcome-driven, optimizing for business impact rather than benchmark scores\n\nML / RL / LLM experience with strong Python skills\n\nComfort with typed backends, async pipelines, and integrations\n\nExcellent communication with both technical and non-technical stakeholders, and comfort operating in an ambiguous, fast-paced environment\n\nPreferred qualifications\nExperience with fine-tuning, tool use, multi-agent orchestration, or enterprise-scale RL\n\nStartup or fast-paced environment experience\n\nWhy Echelon is the right move\nHard problems, real stakes: Apply RL and reasoning to high-value, regulated enterprise environments.\n\nCategory creation: Define how autonomous agents deliver enterprise change.\n\nMassive leverage: Each breakthrough generalizes across thousands of customer workflows.\n\nFounding-team impact: Work directly with the founders and shape Echelon's research roadmap.\n\nLearn from the best: Direct collaboration with ex-Moveworks AI leaders building autonomous enterprise AI.\n\nCompensation & Logistics\nCompetitive, fair-market compensation based on your experience and location\n\nEligible for meaningful early-stage equity\n\nFully remote, with occasional travel for team gatherings (travel and logistics costs covered by Echelon)\n\nYou'll work daily with frontier AI models and our own agent platform\n\nHow we hire (fast and human)\nIntro call with the team (30 minutes)\n\nTechnical deep-dive on your ML/RL experience, plus a working session\n\nMeet our founders to learn about the vision and the strategic projects ahead\n\nDecision, typically within two weeks of first contact","description_format":"text","description_chars":5480,"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":["AI Consulting & Integration","Enterprise Apps Implementation 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