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About This Role
### About Crexi
Crexi is reimagining commercial real estate with an AI\-powered platform built to deliver smarter, more efficient solutions at every stage of the deal lifecycle. From real\-time data and market insights generated by Crexi Intelligence, to targeted property marketing and seamless deal management through Crexi PRO, and a transparent, time\-bound bidding experience with Crexi Auction— Crexi enables users to evaluate opportunities, maximize exposure, and close with speed and confidence. To date, Crexi has facilitated over $1 trillion in transactions, 8\.6 billion square feet leased, and supports a growing community of more than 2 million monthly active users.
Crexi's mission is to catalyze the next generation of commercial real estate through three core pillars: Access, Innovation, and Connection. Crexi's platform democratizes CRE by providing unprecedented access to market insights and opportunities, accelerates CRE dealmaking with purpose\-built technology that enhances speed and transparency; and empowers CRE professionals with a centralized platform designed for real\-time collaboration and success.
About the Role:
The AI Engineer builds the agentic AI systems that power Crexi's platform, including orchestration, retrieval, and action layers grounded in Crexi's proprietary commercial real estate data. The role builds multi\-step agentic workflows that understand user intent, orchestrate the right capabilities, and complete real work, not just answer questions, using tools like LangGraph and AWS Bedrock AgentCore. It exists to extend Crexi's AI\-powered research, document generation, and zoning intelligence into unified, trustworthy agentic experiences for brokers, appraisers, lenders, and investors.
What You'll Do:
A typical day may include:
- Design and implement multi\-step agentic workflows, including routing, planning, tool use, and state management, using frameworks like LangGraph/LangChain to move from user intent to real, completed actions.
- Build evaluation frameworks and production monitoring (offline evals, human\-in\-the\-loop annotation) to measure quality, reliability, and business outcomes, not just model metrics.
- Build context and retrieval pipelines over Crexi's proprietary CRE data, handling the compound, qualitative queries that structured filters can't answer.
- Work primarily with Anthropic frontier models on AWS Bedrock, using Bedrock AgentCore for agent runtime, identity, and memory, and helps evaluate open\-weight alternatives on cost, latency, and capability.
- Implement guardrails, structured outputs, and graceful ambiguity handling so agentic systems ask for clarification rather than acting on unclear intent, and establishes trace\-level observability across LLM calls and tool invocations.
- Leverage agentic coding tools (e.g., Claude Code, Codex) for scaffolding, refactoring, test generation, and debugging, while critically reviewing AI\-generated output for correctness, security, and performance.
- Rapidly prototype against real user feedback, partnering with Product and Design, and helps convert successful prototypes into scalable, reusable capabilities.
- Ensure AI outputs are explainable, source\-linked, and trustworthy enough to inform real business decisions.
Qualifications:
- 5\+ years of professional software development experience in production environments with meaningful scope/ownership, including 2\+ years hands\-on building LLM\-powered applications in production.
- Real experience with agentic architectures (multi\-step pipelines, tool/function calling, state machines, memory), ideally with LangGraph or similar orchestration frameworks.
- Strong retrieval design skills: RAG systems, embeddings, vector search, and the judgment to know when retrieval is the problem and when the data is.
- Strong Python proficiency and real\-world experience building backend services and APIs in production.
- Experience with cloud infrastructure (AWS preferred) and modern collaborative workflows (GitHub/GitLab, code reviews, CI/CD).
- Demonstrated use of agentic coding tools (e.g., Claude Code, Codex) in a professional workflow, without compromising quality.
- Comfortable operating in ambiguity and fast iteration cycles; experience designing LLM evaluation pipelines (offline evals, production monitoring, human\-in\-the\-loop annotation) is a plus.
- Bachelor's degree in Computer Science or related field, or equivalent practical experience.
Who You Are:
- Thrives in a fast\-paced, dynamic environment.
- Exceptional communication skills, with the ability to provide clear and concise information to stakeholders at all levels of the organization.
- Strong analytical skills, with the ability to interpret complex information, identify patterns, and present insights clearly and accurately.
- Excellent organizational and prioritization skills, with the ability to manage multiple priorities and deadlines simultaneously.
- Self\-starter who independently owns their own success and demonstrates strong initiative.
- Responsive, action\-oriented, and proactive in identifying and resolving issues.
- Strong problem\-solving and creative thinking skills, with the ability to evaluate options and implement effective solutions.
- Effective collaborator and team player, with the ability to work cross\-functionally and build strong working relationships.
- Demonstrated ability to handle sensitive and confidential information with the utmost professionalism and discretion.
Why Crexi?
- Rapidly growing startup with a dynamic work environment
- Flexible team structure with the ability to progress in career
- Health, Dental, and Vision insurance
- Collaborative culture and numerous team activities
The anticipated base salary range for candidates who will work in our Playa Vista, California location is $179,000 to $241,000\. The final salary offered to a successful candidate will depend on several factors, which may include, but are not limited to, the type and length of experience applicable to the role and within the industry, education, geographic location, etc. Commercial Real Estate Exchange, Inc ("Crexi") is a multi\-state employer, and this salary range may not reflect positions that work in other states.
Crexi is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Crexi will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.
Salary Context
This $179K-$241K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CREXi, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $179K to $241K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
CREXi AI Hiring
CREXi has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $241K - $241K.
Location Context
AI roles in Los Angeles pay a median of $214,112 across 708 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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