Interested in this AI/ML Engineer role at Marcus & Millichap?
Apply Now →Skills & Technologies
About This Role
Marcus \& Millichap, the nation’s leading commercial real estate investment brokerage firm, is seeking a Senior Financial Analyst, AI \& Financial Analytics to support a high\-performing brokerage team in the New York City office. This role sits at the intersection of brokerage operations, financial analysis, and advanced technology, with a strong emphasis on implementing AI\-driven solutions to enhance efficiency, insight generation, and scalability across the business
The base salary range for this role is $38\.46 – $52\.89 per hour. Actual pay will be determined by factors such as education, experience, and job\-related skills relevant to the role. Marcus \& Millichap is a multi\-state employer and posted salary ranges are provided in good faith for compliance with all relevant pay\-transparency regulations. These ranges may not reflect potential compensation for positions based in other states or regions.
This position will be full time in the New York City office.
### Responsibilities
- Underwrite commercial real estate transactions, including single\-tenant and multi\-tenant assets
- Develop advanced financial models, pricing analysis, and valuation based on market data
- Analyze financial statements, rent rolls, and investment metrics to support deal strategy
- Partner with brokers to deliver data\-driven insights and support client presentations
- Identify and implement AI solutions to automate workflows, reduce manual processes, and improve efficiency
- Design and deploy automation tools for deal tracking, reporting, and pipeline management
- Optimize CRM platforms (Salesforce or similar) using AI\-enabled workflows and data integrations
- Leverage data analytics and machine learning tools to generate actionable business intelligence
- Develop dashboards and reporting systems to track performance, productivity, and revenue\-driving activities
- Lead adoption and training of new AI and technology tools across brokerage teams
- Partner with leadership to drive digital transformation initiatives and operational strategy
- Collaborate with marketing, research, and operations teams to support transaction execution and business development
### Qualifications
- Bachelor’s degree in Real Estate, Finance, Business, Data Science, or related field
- 5–10\+ years of experience in real estate, finance, operations, consulting, or technology\-driven roles
- Strong financial modeling, underwriting, and analytical capabilities
- Advanced proficiency in Excel; experience with Argus preferred
- Experience with CRM platforms such as Salesforce and data systems
- Hands\-on experience with AI tools, automation platforms, or data analytics technologies
- Familiarity with workflow automation tools (e.g., Power Automate, Zapier) and data visualization tools (e.g., Power BI, Tableau) preferred
- Strong communication skills with ability to translate data into actionable insights
- Highly organized, detail\-oriented, and capable of operating in a fast\-paced environment
### Benefits
Marcus \& Millichap offers a comprehensive benefits package designed to support employees’ health, well\-being, and financial security. Eligible employees may have access to the following benefits, subject to the terms of applicable benefit plans:
- Health, dental, and vision insurance
- Paid time off and paid holidays
- 401(k) retirement plan with employer matching contributions
- Life and disability insurance
- Employee Assistance Program (EAP) and wellness resources
Benefits eligibility, coverage, and employer contributions may vary based on position, employment status, location, and length of service. This summary is provided for informational purposes only and does not constitute a guarantee of benefits. Additional details will be provided during the hiring process or upon request.
Founded in 1971, Marcus \& Millichap (NYSE: MMI) is a leading commercial real estate brokerage firm focusing exclusively on investment sales, financing, research, and advisory services, with nearly 1,700 investment sales and financing professionals in 80\+ offices throughout the United States and Canada.
Our mission is to help our clients create and preserve wealth by providing the best real estate investment sales, financing, research, and advisory services available.
Marcus \& Millichap, Inc. is a leading national real estate services firm specializing in commercial real estate investment sales, financing services, research and advisory services. As of December 31, 2025, the Company had 1,808 investment sales and financing professionals in more than 80 offices who provide investment brokerage and financing services to sellers and buyers of commercial real estate. The Company also offers market research, consulting and advisory, and leasing services to its clients. Marcus \& Millichap, Inc. closed 8,818 transactions in 2025, with a sales volume of $50\.8 billion.
Marcus \& Millichap is an Equal Opportunity Employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under federal, state, or local law.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $79K-$108K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Marcus & Millichap, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($93K) sits 48% below the category median. Disclosed range: $79K to $108K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Marcus & Millichap AI Hiring
Marcus & Millichap has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Manhattan, NY, US. Compensation range: $108K - $108K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.