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About This Role
Posting Type
Hybrid/Remote
Job Overview
WHO WE ARE
Relativity 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.
The 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 are committed to expanding access to technology and supporting pro bono legal work.
WHAT WE DO
Gavel is the Microsoft Word-native agentic surface where lawyers perform real-time, evidence-based document editing and drafting. Gavel operates as a small, founder-led team within Relativity, with product and engineering led directly by Dorna Moini, CEO, and Pierre Martin, CTO of Gavel and VP of Engineering at Relativity. Job Description and Requirements
ABOUT THE ROLE
As a Senior Software Engineer, you will build and own production agents end to end across the full stack. You will take agents from customer problems and rough specifications through tool design, prompt and context work, evaluation sets, Word or web experiences, feature-flagged launches, and follow-up improvements informed by acceptance data.
You will work independently on well-scoped problems and collaboratively on ambiguous ones, directly with the founders and the rest of the team.
- Level: Senior
- Reports to: Lead or Staff Engineer, Gavel at Relativity
- Location: Remote within the United States, Seattle, or Chicago
WHAT YOU’LL DO
- Design, build, and ship agents and agent capabilities end to end, including tools, prompts, context assembly, structured outputs, guardrails, services, and the frontend experience in Word or on the web.
- Build and maintain evaluations for the capabilities you own. Use them to guide decisions and instrument production to measure acceptance, latency, and cost per agent.
- Contribute to shared infrastructure, including the harness, context and retrieval pipeline, model routing, and observability stack.
- Practice a modern software development lifecycle through small, frequent commits, feature-flagged production deployments, fast rollbacks, and daily use of agentic engineering tools.
- Debug non-deterministic failures, retrieval misses, tool-call errors, and model regressions.
- Participate in design reviews, write clear technical documentation, review code for other engineers, and mentor Advanced engineers.
- Work directly with product, design, and, at times, customers to understand what good work product means for a given legal task.
WHAT WE’RE LOOKING FOR
Required
- 5+ years of experience building production software in service-oriented, continuously deployed teams, including experience shipping LLM-powered features to real users through a launched project, not only experiments.
- Direct experience building full-stack web applications and agentic applications, including API usage across providers, function calling and structured outputs, prompt and context engineering, retrieval-augmented generation, and evaluation fundamentals.
- Hands-on cloud service development experience with AWS, Azure, or an equivalent platform, including containers, CI/CD, and production debugging, with a willingness to develop deep expertise .
- Fluency in TypeScript or Python and the ability to work across a React frontend, Node or Python services, and PostgreSQL.
- Experience writing and maintaining tests that catch regressions.
- Strong engineering fundamentals in API design, data modeling, concurrency, performance, and reliability.
- Good written and spoken English, with the ability to explain technical decisions succinctly to non-engineers.
- A strong commitment to simplicity, demonstrated through open-source work, side projects, or past professional work.
Preferred
- [Insert preferred qualifications, if applicable]
WHY WE COULD BE A GREAT FIT
Impactful Mission
- Build systems that help customers organize data, discover the truth, and act on it in high-stakes legal matters.
Engineering at Scale
- Work on distributed, cloud-native systems that process large volumes of data.
Cutting-Edge Technology
- Build with AI, cloud platforms, and scalable architectures shaping legal tech.
Growth and Ownership
- Gain experience owning systems end-to-end across cloud and distributed environments.
Collaborative Culture
- Work in a team focused on knowledge sharing and continuous improvement.
Inclusive Environment
- Diverse perspectives create stronger teams and better outcomes.
Compensation and Benefits
- Competitive salary, benefits, DTO, parental leave, and equity program .
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:
$126,000 and $190,000 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:
Automation, Data Analysis, Database Management, Network Architecture, Performance Optimizations, Problem Solving, Project Management, Software Development, System Designs, Technical Leadership
Salary Context
This $126K-$190K range is below the median for AI Software Engineer roles in our dataset (median: $192K across 146 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 8,943 AI roles we're tracking, AI Software Engineer positions make up 4% of the market. At Relativity, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($158K) sits 28% below the category median. Disclosed range: $126K to $190K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Relativity AI Hiring
Relativity has 7 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer. Based in IL, US. Compensation range: $155K - $262K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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