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About This Role
AI Tools Manager, Matterport - Arlington, VA
Job Description
Who is CoStar Group?
CoStar Group (NASDAQ: CSGP) is a leading global provider of commercial and residential real estate information, analytics, and online marketplaces. Included in the S&P 500 Index, CoStar Group is on a mission to digitize the world’s real estate, empowering all people to discover properties, insights and connections that improve their businesses and lives.
We have been living and breathing the world of real estate information and online marketplaces for over 35 years, giving us the perspective to create truly unique and valuable offerings to our customers. We’ve continually refined, transformed and perfected our approach to our business, creating a language that has become standard in our industry, for our customers, and even our competitors. We continue that effort today and are always working to improve and drive innovation. This is how we deliver for our customers, our employees, and investors. By equipping the brightest minds with the best resources available, we provide an invaluable edge in real estate.
Who is Matterport?
Matterport is leading the digital transformation of the built world. Our spatial computing platform turns buildings into data, making every space more valuable and accessible. Millions of buildings in more than 170 countries have been transformed into immersive Matterport digital twins, powering use cases across marketing, documentation, construction, facilities management, and operations. Matterport also designs and sells industry-leading camera hardware through direct-to-consumer ecommerce and major online marketplaces.
Role Overview: AI Tools Manager
Arlington, VA | In office, Monday – Friday
We are seeking an AI Tools Manager to build and scale the AI capabilities Matterport’s sellers use every day. This is a hands-on builder role who initially owns two products: the AI Sales Assistant, which generates pre-call briefings and account research for Account Executives, and an AI BDR agent that helps AEs identify prospects and run initial templated outreach.
This role owns the roadmap and implementation for both, from current state through scaled deployment across the sales organization. The work spans discovery and product definition, roadmap prioritization and trade-off calls, implementation and integration with the systems sellers already use, and adoption across the sales team . Success is measured in seller behavior and pipeline impact.
Those two products are the starting point, not the boundary. This role also owns the strategy behind the roadmap: researching and proposing a fully integrated AI go-to-market strategy across the entire Matterport tool set, and proposing and building the new tools that strategy calls for. We are looking for someone who wants to define where AI takes our revenue motion over the next several years, not only execute the two initiatives in front of us today.
The work is equal parts product strategy, product management, systems integration, and enablement. It suits someone who has built AI into the daily workflow of a sales team, and who now wants a bigger canvas and real room to shape the outcome.
Responsibilities
AI Go-to-Market Strategy
- Research and propose a fully integrated AI go-to-market strategy spanning the full Matterport sales tool set, from prospecting through onboarding, expansion, and renewal, and maintain it as a living point of view rather than a one-time deliverable.
- Identify the white space where no tool exists today, then propose, scope, and drive the development of new capabilities to fill it.
- Track the AI landscape continuously — models, agentic frameworks, vendors, and pricing — and translate what changes into concrete recommendations for Matterport GTM.
- Bring a defensible view to Matterport and CoStar Group leadership on where AI creates real leverage in the revenue motion, and where it does not.
Partner cross functionally to drive AE adoption as the primary measure of success, through onboarding, training, and in-workflow support. *
AI Sales Assistant
- Own the roadmap, functional upgrades, and release cadence for the AI Sales Assistant, the AE-facing tool that produces pre-call briefings, account research, and pain hypotheses grounded in Matterport proof points.
- Set the standard for the product: usage targets, output quality thresholds, and the review cadence that holds it to them.
- Keep the underlying content current, including customer proof, vertical narratives, and competitive positioning, in partnership with Product Marketing.
- Run structured feedback loops with AEs and Sales leadership and ship improvements on a predictable cycle.
- Run continuous discovery — call reviews, ride-alongs, and usage analysis — and turn what you learn into a prioritized backlog with clear problem statements, success criteria, and explicit trade-offs.
Define and measure ROI for the AI Sales Assistant by connecting usage, seller time savings, meeting preparation quality, pipeline creation, and conversion impact to clear business outcomes. *
AI BDR Agent and Outbound Automation
- Build and scale an AI BDR agent that helps AEs identify and prioritize prospects and initiate templated outreach.
- Own the outreach standard: sequence libraries, message templates, personalization logic, and guardrails for tone, brand, and compliance.
- Integrate agent workflows with ZoomInfo, Gong, Salesforce, and the broader outbound stack, including the data quality and enrichment logic that determines targeting accuracy.
- Establish testing discipline through message and sequence experimentation, control groups, and reporting against reply, meeting, and pipeline conversion.
Define and measure ROI for outbound automation by connecting agent usage, prospecting efficiency, reply rates, meeting creation, pipeline contribution, and conversion impact to clear business outcomes. *
Systems, Data, and Measurement
- Instrument both products. Report usage, output quality, and downstream pipeline impact to Sales and Marketing leadership on a monthly cadence.
- Own tooling decisions and vendor relationships in line with CoStar Group standards.
- Make build, buy, and partner calls on models and tooling, with written rationale covering cost, latency, data handling, and switching risk.
Partner with CoStar Group IT on architecture, security review, and data governance as these capabilities scale. *
Cross-Functional Partnership
- Serve as the connection point between Sales, Sales Engineering, Marketing and Product Marketing, Revenue Operations, and CoStar Group IT.
- Translate seller needs into technical requirements, and technical constraints back into terms sellers and leadership can act on.
Represent Matterport’s AI-for-sales work in CoStar Group AI forums and contribute to group-level direction. *
Basic Qualifications
- Bachelor’s degree required from an accredited, not-for-profit, in-person university or college.
- 5+ years of professional experience
- 3+ years in product management or an engineering role, with hands-on ownership of a product or feature through discovery, definition, build, launch, and iteration.
- Demonstrated hands-on experience putting AI or LLM-based workflows into production, including prompt design, evaluation methodology, workflow automation, and iteration against usage data.
- Demonstrated AI Coding and Troubleshooting Agents: Build and run coding agents that write, test, and maintain the scripts, automations, and connectors that keep Matterport's go-to-market systems running.
- Track record owning adoption of a tool or process across a distributed sales team with measurable usage and business outcomes.
- Product judgment: able to define scope, write crisp requirements, sequence a roadmap, and decline work that does not serve the outcome.
- Comfort operating as an individual contributor with cross-functional influence.
- Strong written and verbal communication, credible with both Account Executives and technical partners.
A track record of commitment to prior employers. *
AI and Agentic Systems Depth
- Hands-on experience with automations: recurring and scheduled tasks, inbound webhooks, and event triggers.
- Familiarity with LLM APIs across cloud providers, including routers and fallback mechanisms.
- Experience using state-of-the-art frontier models and their open-weight counterparts, and with the risks and trade-offs of choosing between them.
- Able to explain the difference between training and inference, and why hardware trade-offs that make sense for one do not make sense for the other.
- Understanding of the major bot and integration interfaces to enterprise tools, including corporate email, Slack, and Salesforce.
Familiarity with MCP (Model Context Protocol) and how to use it to build solutions. *
Preferred Skills and Qualifications
- Experience deploying AI agents for outbound prospecting or seller productivity at scale.
- Experience working directly with go-to-market functions: Sales, Sales Enablement, Revenue Operations, or Sales Engineering. Breadth across several of these counts for more than depth in only one.
- Familiarity with enterprise AI governance, security review, and data privacy requirements in a public-company environment.
- Working fluency with the modern sales stack, including Salesforce, ZoomInfo, Gong, and outbound sequencing platforms.
- Experience in commercial real estate, AEC, proptech, or adjacent B2B technology sectors.
- Prior experience partnering with a central IT or platform organization on shared infrastructure.
Experience running an agent-based support or incident response workflow for internal, enterprise business systems.
*
What’s In It For You?
If you are a driven professional looking for a high-growth, high-reward career, CoStar Group offers the ideal opportunity. Be part of a best-in-class company with strong year-over-year growth that invests in your success. Enjoy a rewarding atmosphere where you can learn, excel, and grow.
When you join CoStar Group, you’ll experience a collaborative and innovative culture working alongside the best and brightest to empower our people and customers to succeed.
We offer you generous compensation and performance-based incentives. CoStar Group also invests in your professional and academic growth with internal training and tuition reimbursement.
Our benefits package includes (but is not limited to):
- Comprehensive healthcare coverage: Medical / Vision / Dental / Prescription Drug
- Life, legal, and supplementary insurance
- Virtual and in person mental health counseling services for individuals and family
- Commuter and parking benefits
- 401(K) retirement plan with matching contributions
- Employee stock purchase plan
- Paid time off
- Tuition reimbursement
- On-site fitness center and/or reimbursed fitness center membership costs (location dependent)
- Access to CoStar Group’s Employee Resource Groups
Complimentary gourmet coffee, tea, hot chocolate, fresh fruit, and other healthy snacks *
Sponsorship
We welcome all qualified candidates who are currently eligible to work full-time in the United States to apply. However, please note that CoStar Group is not able to provide visa sponsorship for this position.
The final salary or hourly rate offered for this role will fall within the range set forth below based on a variety of factors, including but not limited to, geographic location, skills, and competencies.
Base Compensation: $97,800.00 - $154,000.00 Annually
#LI-BR1
#Matterport
CoStar Group is an Equal Employment Opportunity Employer; we maintain a drug-free workplace and perform pre-employment substance abuse testing
Salary Context
This $97K-$154K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CoStar Group, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($125K) sits 41% below the category median. Disclosed range: $97K to $154K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
CoStar Group AI Hiring
CoStar Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $154K - $154K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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