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About This Role
At Compass, our mission is to help everyone find their place in the world. Founded in 2012, we're revolutionizing the real estate industry with our end\-to\-end platform that empowers residential real estate agents to deliver exceptional service to seller and buyer clients.
About the Role
The Engineering Platform organization owns the systems that engineers at Compass depend on to build, test, deploy, and operate software. Our mission is to reduce friction across the SDLC, improve engineering effectiveness, and build the foundation for AI\-enabled software development.
This role is centered on maximizing engineering velocity and velocity at scale by architecting fast feedback loops, optimizing system efficiency, and building seamless, intuitive workflows that empower developers across the entire software development lifecycle. As a Staff Engineer, you will work across observability, engineering telemetry, developer tooling, software catalog systems, organizational metadata, and AI\-assisted workflows to turn these gaps into durable platform capabilities.
Applying AI across the software development lifecycle is a core responsibility of this role. You will build systems that use engineering signals to improve feedback loops, automate operational decision\-making, and reduce manual engineering work. This includes capabilities such as intelligent testing frameworks, AI\-assisted production troubleshooting, automated change analysis, workflow orchestration, and secure execution environments for AI\-generated code integrated throughout the SDLC.
The ideal candidate sees the engineering ecosystem as an interconnected system of services, workflows, operational signals, and human behavior \- and knows how to simplify, standardize, and scale it.
Responsibilities
- Define the technical direction for Developer Intelligence systems across the SDLC, including engineering telemetry, observability, and AI\-assisted developer tooling.
- Identify systemic engineering inefficiencies by analyzing developer workflows, operational signals, and SDLC feedback loops, then translate those findings into scalable platform capabilities.
- Architect systems that collect, correlate, and operationalize data from CI/CD pipelines, source control, testing infrastructure, production systems, and developer workflows.
- Build AI\-assisted capabilities across the SDLC, including code generation, code review, software validation, production troubleshooting, and workflow automation.
- Lead large cross\-functional initiatives by aligning teams, driving architectural direction, and decomposing ambiguous problems into scalable execution plans.
- Identify and eliminate systemic bottlenecks impacting engineering velocity, reliability, operational efficiency, and developer experience.
- Mentor engineers, raise technical standards, and contribute to a strong engineering culture through design leadership and high\-quality code review practices.
Basic Qualifications
- BS or higher in Computer Science, Software Engineering, or equivalent practical experience.
- 10\+ years of professional software development experience, with a proven track record of leading large\-scale technical initiatives.
- Strong experience designing, scaling, or operating data pipelines, telemetry systems, large\-scale observability infrastructure, or developer metrics frameworks (e.g., tracking DORA or SPACE metrics).
- Hands\-on professional experience integrating AI tools \& capabilities into software engineering, automated workflows, or system triaging tools.
- Experience designing and operating platform systems on major cloud infrastructures (AWS, GCP, or Azure).
- Experience modernizing legacy systems, consolidating fragmented tooling, and driving platform standardization across multiple engineering teams.
- Strong track record of technical leadership, organizational influence, and cross\-functional execution.
- Proficiency in Go and professional experience in one or more of the following: Java, Python, or TypeScript.
Preferred Qualifications
- Deep familiarity with core SDLC infrastructure \- including practical experience with or exposure to build systems, local development environments, testing infrastructure, or CI/CD pipelines.
- Experience driving platform adoption or data consolidation across an engineering organization of 1,000\+ developers.
- A strong track record of building tools for developers \- intimately understanding internal customer pain points and translating feedback into reliable platform capabilities.
Compensation:
The base pay range for this position is $205,000 \- $228,000 annually; however, base pay offered may vary depending on job\-related knowledge, skills, and experience. Bonuses and restricted stock units may be provided as part of the compensation package, in addition to a full range of benefits. Base pay is based on market location. Minimum wage for the position will always be met.
Perks that You Need to Know About:
Participation in our incentive programs (which may include eligible cash, equity, or commissions). Plus paid vacation, holidays, sick time, parental leave, and recharge leave; medical, tele\-health, dental and vision benefits; 401(k) plan; flexible spending accounts (FSAs); commuter program; life and disability insurance; Maven (a support system for new parents); Carrot (fertility benefits); UrbanSitter (caregiver referral network); Employee Assistance Program; and pet insurance.
Do your best work, be your authentic self.
At Compass, we believe that everyone deserves to find their place in the world — a place where they feel like they belong, where they can be their authentic selves, where they can thrive. Our collaborative, energetic culture is grounded in our Compass Entrepreneurship Principles and our commitment to diversity, equity, inclusion, growth and mobility. As an equal opportunity employer, we offer competitive compensation packages, robust benefits and professional growth opportunities aimed at helping to improve our employees' lives and careers.
Notice for California Applicants
Los Angeles County Fair Chance Notice
Salary Context
This $205K-$228K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $183K across 194 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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Compass Group, 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 $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $205K to $228K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Compass Group AI Hiring
Compass Group has 2 open AI roles right now. They're hiring across AI Software Engineer. Based in New York, NY, US. Compensation range: $166K - $228K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
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