Staff Software Engineer

Posting Type

Hybrid/Remote

Job Overview

Job Description and Requirements

About the Role

Discovery 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.

You 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.

What You'll Do

• Staff 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.
• Own 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.
• Set 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.
• Own token economics and latency budgets.Instrument cost and latency per workflow, set unit targets, and decide routing, caching, and model tier from data.
• Design 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.
• Architect 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.
• Engineer for defensibility.Citation fidelity, privilege and confidentiality controls, human review paths, and the auditability a customer needs to defend a workflow under challenge.
• Catch 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.
• Raise the bar across teams.Design review, reference implementations, documented paths from proof of concept to production, and mentorship of senior and lead engineers.
• Collaborate 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.

What We're Looking For

Required

• 7+ years building and operating large-scale distributed cloud systems.
• 3+ years shipping production model-backed systems that real customers depend on. Prototypes and demos will not be enough.
• Depth across LLMs, RAG, agentic workflows, prompt and context engineering, vector and hybrid search, orchestration frameworks, and observability for non-deterministic systems.
• You have owned an evaluation practice. You built the eval sets, metrics, and gates that told a team whether a change helped or hurt.
• Architecture accountability across cloud services and data pipelines in multi-tenant SaaS, including scalability, reliability, and security.
• Quantitative reasoning for model behavior: eval metrics, error analysis, and tradeoffs across accuracy, latency, throughput, and cost.
• Technical 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.

Preferred

• Legal technology, eDiscovery, search, document intelligence, or another domain where output must be defensible and auditable.
• Agentic and multi-agent systems, including consolidating specialized agents into a generalist architecture without losing task quality.
• MCP servers or model-facing tools and skill interfaces.
• Cutting inference cost at scale through routing, distillation, caching, or smaller fine-tuned models.
• Governance, monitoring, and traceability practices in regulated or high-trust environments.
• Azure, Kubernetes, CosmosDB, and progressive delivery through feature flagging.

Relativity is committed to competitive, fair, and equitable compensation practices.

This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between following values:

The 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.

Required Skills: