{"id":2831228,"url":"https://alion.io/job/relativity-staff-ai-engineer-2","title":"Staff AI Engineer","company":{"id":2576,"name":"Relativity","domain":"relativity.com","url":"https://alion.io/company/relativity","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":45,"computed_at":"2026-10-21T05:46:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":174000,"max":262000,"currency":"USD","period":"year","gross":null,"usd_annual":262000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"Azure Cosmos DB","optional":false},{"name":"Context Engineering","optional":false},{"name":"Hallucination","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Progressive Delivery","optional":false},{"name":"RAG","optional":false},{"name":"Structured Outputs","optional":false}],"status":"live","first_seen_at":"2026-09-30T00:00:00Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-21T10:55:54Z","board_verified":true,"closed_at":null,"days_open":21,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":21},"description":"Posting Type\nHybrid/Remote\nJob Overview\nWho We AreRelativity is a leading legal data intelligence company building technology that helps users organize data, discover the truth, and act on it with confidence. Our AI-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high-stakes legal work where accuracy and trust are crucial.\nThe world’s largest law firms, corporations and government agencies rely on Relativity’s legal AI software to securely surface and manage the most relevant and impactful information in their matters. Beyond our commercial impact, we’re deeply committed to expanding access to technology for academic institutions through Relativity Academic and supporting pro bono legal work through Justice for Change.\nWhat We Do\nAt Relativity, engineers don’t just write code- they harness AI tools to architect, optimize, and deliver scalable software faster and shape the future of legal technology. They shape how industries uncover critical insights, ensure compliance, advance access to justice and continuously evolve our platform using the latest technologies, including cloud-native architectures, AI capabilities, and modern developer tooling. Our AI powers privacy programs, identifies confidential information, and detects language indicating misconduct. We’re reimagining how legal professionals uncover truth at scale, and engineering is at the heart of that transformation.\nIf you’re passionate about solving hard problems, designing for scale, and building tools that matter-while working in a culture that values transparency, innovation, and shared success- Relativity offers the opportunity to make a measurable difference in the marketplace.\nJob Description and Requirements\nAbout the Role\nDiscovery builds the products at the center of Relativity: Document Review, aiR for Case Strategy, Intelligent Fact Gen, Memo Gen, Proof Charting, Translate, and Redact. They reason over petabyte-scale legal corpora, and their output must hold up in front of counsel, opposing counsel, and a court. What they surface and what they miss shapes the outcome of real cases.\nYou will own how these systems are built, evaluated, and operated. This is a hands-on role across the whole Discovery portfolio. Not research, not management.\nWhat You'll Do\nStaff engineers here set technical direction across teams and build the hardest parts with them, from prototype through production. Knowing which designs to hand to a team and which to carry yourself is part of the job. You are not running a single team's backlog.\nOwn the inference path end to end. Retrieval, context construction, model selection and routing, agent orchestration, tool interfaces, structured output, graceful degradation, and observability that makes non-deterministic failure debuggable at 2am.\nSet the evaluation bar. Golden sets, offline suites, regression gates in CI, online quality telemetry. A change to a prompt, a model, or a retrieval strategy gets measured, not argued.\nOwn token economics and latency budgets. Instrument cost and latency per workflow, set unit targets, and decide routing, caching, and model tier from data.\nDesign for reuse. Decouple capability logic from its delivery harness behind explicit interface contracts, so a single capability may be shipped as a service, an MCP skill, or an embedded experience without a rewrite.\nArchitect the data and retrieval layer. Batch and streaming pipelines, indexing and embedding strategy, hybrid search, tenant isolation, and lineage that traces an answer back to the document it came from.\nEngineer for defensibility. Citation fidelity, privilege and confidentiality controls, human review paths, and the auditability a customer needs to defend a workflow under challenge.\nCatch drift before customers do. Quality regressions, hallucination and refusal rates, latency and cost creep. Lead root cause analysis, eliminate recurrence, and publish the learning across Discovery.\nRaise the bar across teams. Design review, reference implementations, documented paths from proof of concept to production, and mentorship of senior and lead engineers.\nCollaborate across domains. Work with Applied Science, Product, Design, and peer domains, and drive calls to closure where you hold no formal authority. Push coding agents and modern SDLC practice into the teams without lowering the engineering bar.\nWhat We're Looking For\nRequired\n7+ years building and operating large-scale distributed cloud systems.\n3+ years shipping production model-backed systems that real customers depend on. Prototypes and demos will not be enough.\nDepth across LLMs, RAG, agentic workflows, prompt and context engineering, vector and hybrid search, orchestration frameworks, and observability for non-deterministic systems.\nYou have owned an evaluation practice. You built the eval sets, metrics, and gates that told a team whether a change helped or hurt.\nArchitecture accountability across cloud services and data pipelines in multi-tenant SaaS, including scalability, reliability, and security.\nQuantitative reasoning for model behavior: eval metrics, error analysis, and tradeoffs across accuracy, latency, throughput, and cost.\nTechnical leadership across teams without formal authority, and the writing skills to put a decision, its alternatives, and its tradeoffs in front of executives and get an answer.\nPreferred\nLegal technology, eDiscovery, search, document intelligence, or another domain where output must be defensible and auditable.\nAgentic and multi-agent systems, including consolidating specialized agents into a generalist architecture without losing task quality.\nMCP servers or model-facing tools and skill interfaces.\nCutting inference cost at scale through routing, distillation, caching, or smaller fine-tuned models.\nGovernance, monitoring, and traceability practices in regulated or high-trust environments.\nAzure, Kubernetes, CosmosDB, and progressive delivery through feature flagging.\nRelativity is committed to competitive, fair, and equitable compensation practices.\nThis position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.\nThe expected salary range for this role is betweenfollowing values:\n$174,000 and $262,000The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.\nRequired Skills:\nAlgorithms, Automation, Debugging, Distributed Systems, Performance Tuning, Problem Solving, Project Management, Software Development, System Designs, Technical Leadership","description_format":"text","description_chars":6898,"description_truncated":false,"requirements":{"experience_years_min":3,"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":["Document Management","Legal AI","Legal Software"],"lifecycle":[{"event":"open","at":"2026-10-19T03:20:11Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.951,"p_room":0.9,"age_days":21,"expected_fill_days":45,"reasons":["conf:6","urgency","velocity","win:mid"],"computed_at":"2026-10-21T05:46:15Z"},"pay":{"stated_usd_annual":262000,"is_top_pay":false},"html_url":"https://alion.io/job/relativity-staff-ai-engineer-2","json_url":"https://alion.io/job/relativity-staff-ai-engineer-2.json","meta":{"generated_at":"2026-10-21T16:12:42Z","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":22948,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-22T00:00:00Z"}},"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":2576},"rest":"https://alion.io/mcp/rest/get_company?id=2576"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Frelativity-staff-ai-engineer-2"},{"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%2Frelativity-staff-ai-engineer-2"},{"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%2Frelativity-staff-ai-engineer-2"},{"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/relativity-staff-ai-engineer-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Frelativity-staff-ai-engineer-2"}]}