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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.
Engineering @ Compass
Compass has built the first modern end-to-end real estate platform by integrating agents, buyers and sellers through technology. Until Compass, no one has achieved the blend of the Natural Intelligence that hundreds of thousands of enterprising real estate agents bring to this market, with the "Artificial Intelligence" that cloud, mobile and AI technologies enable. We are building AI to empower AI - Artificial Intelligence to empower Agent Intelligence.
As one of the fastest growing technology companies of our generation, in an industry larger than any other, we have an opportunity and obligation to build a world-class engineering team and the operating platform that will transform real estate.
Security @ Compass
We are hands-on security engineers helping to build secure, resilient, and scalable web apps, mobile apps, and platforms for the real estate industry. We work with a diverse set of teams to provide transparent and automated security tooling and services. We architect secure products, perform simulated attacks, identify weaknesses, and partner with teams to remediate and protect.
The Role
Compass is embedding generative and agentic AI directly into the products our agents and clients use every day. This role owns the security of that surface. You will be the technical authority on how Compass builds AI features safely, from the model and retrieval layer, through agent tool invocation, to what ultimately reaches a client's screen. You will work shoulder-to-shoulder with the teams building the AI platform so that platform standards and security standards are one bar, not two.
What You Will Do
- Own the AI product security roadmap. Define the multi-year vision, strategy, and standards for securing AI capabilities in the Compass platform, developed jointly with the AI platform and product engineering teams.
- Threat model AI capability builds. Drive architectural reviews and threat modeling for new AI features, RAG pipelines, multi-agent workflows, and automated decisioning before they ship.
- Build production guardrails. Architect, deploy, and automate real-time protections for LLM inputs and outputs, including injection mitigation, jailbreak defense, output verification, and sensitive-data egress checks on generated responses.
- Constrain autonomy. Design blast-radius controls for autonomous agents: scoped permissions, human-in-the-loop checkpoints for consequential actions, action audit trails, and safe tool-invocation patterns.
- Secure the retrieval layer. Enforce data access controls, source integrity, tenancy boundaries, and retrieval-boundary protections across RAG and vector store implementations.
- Harden the AI supply chain. Assess the security posture, privacy controls, data retention, and training-use terms of third-party foundation models, inference providers, and vector stores used in production.
- Instrument and detect. Build detections and telemetry for AI-specific abuse in production, anomalous tool invocation, prompt abuse patterns, unusual retrieval behavior, and data exfiltration through model responses and integrate them into our monitoring estate.
- Automate the standard. Ship reusable libraries, CI/CD checks, and evaluation harnesses so secure AI patterns are the default path for product engineers rather than a review gate.
- Advise and evangelize. Act as the primary technical consultant for AI architecture decisions and translate emerging AI risk into concrete engineering roadmaps for both engineers and executives.
Who You Are
- Automation is your default approach to security, especially when securing complex AI systems at scale.
- You have a deep working understanding of transformer architectures, inference pipelines, RAG, prompt engineering, and the OWASP Top 10 for LLMs.
- You write production code, strong proficiency in Python or Go, with experience building real integrations and security pipelines, not just prototypes.
- You are proactive about finding and closing gaps in AI/ML workflows through tooling rather than policy.
- You communicate well across audiences, translating emerging AI risk into technical roadmaps for engineers and business impact for leadership.
Minimum Qualifications
- 7+ years in security engineering, with 2+ years focused on AI/ML or LLM application security.
- Demonstrated experience securing a production AI or ML system end to end, development lifecycle, deployment, and runtime.
- Expertise in adversarial ML and LLM risks including prompt injection, model poisoning, data extraction, and unsafe tool use, with hands-on red teaming experience.
- Strong application and cloud security fundamentals: AWS and/or GCP, Kubernetes, IAM, API security, and DevSecOps practices.
- Experience threat modeling complex distributed systems and driving remediation with engineering teams.
- Strong strategic communication skills with a proven ability to translate complex technical AI risk into business impact for executive leadership.
Nice to Have
- Experience with multi-agent autonomous systems or Model Context Protocol (MCP) based architectures.
- Familiarity with MITRE ATLAS, NIST AI RMF, or comparable AI threat/risk frameworks.
- Background building evaluation and safety-testing harnesses for LLM applications.
- Experience in a regulated or consumer-data-heavy environment where model outputs touch customer-facing decisions.
- Prior work on AI platform security architecture or secure-by-default AI SDKs.
The base pay range for this position is $210,000 - $234,000; 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 $210K-$234K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Compass, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($222K) sits 5% above the category median. Disclosed range: $210K to $234K.
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.
Compass AI Hiring
Compass has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $234K - $234K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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