Interested in this AI/ML Engineer role at Rent the Runway?
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About This Role
About Us:
Rent the Runway (RTR) is transforming the way we get dressed by pioneering the world's first Closet in the Cloud. Founded in 2009, RTR has disrupted the $2\.4 trillion fashion industry by inspiring women with a more joyful, sustainable and financially\-savvy way to feel their best every day. As the ultimate destination for circular fashion, the brand now offers infinite points of access to its shared closet via a fully customizable subscription to fashion, one\-time rental or ownership. RTR offers designer apparel and accessories from hundreds of brand partners and has built in\-house proprietary technology and a one\-of\-a\-kind reverse logistics operation. RTR has been named to CNBC's "Disruptor 50" five times in ten years, and has been placed on Fast Company's Most Innovative Companies list multiple times.
About the Team:
Data is core to our growing business and has been ingrained in the company's DNA since its founding. As a member of the Data Analytics team, you will partner closely with Product, Engineering, and other business stakeholders to identify opportunities where machine learning, AI, and advanced analytics can create meaningful customer and business value.
The Data Science team develops machine learning solutions, recommendation systems, experimentation frameworks, and AI\-powered capabilities that directly influence how customers discover, engage with, and rent and buy inventory on Rent the Runway. Our work spans personalization, predictive modeling, decision systems, and intelligent customer experiences.
As RTR continues investing in personalization and AI, Data Science plays a critical role in bringing new customer experiences to life \- from recommendation engines and intelligent styling and fit experiences to future agent\-driven and AI\-powered products.
About the Job:
We are looking for a hands\-on Data Science Manager to lead our growing Data Science function while remaining deeply involved in solving high\-impact machine learning problems. While this role will initially focus on personalization, recommendation systems, and AI\-powered customer experiences, you will also have the opportunity to contribute across a broad range of high\-impact machine learning initiatives as business priorities evolve.
As the technical leader and manager of the Data Science team, you will partner closely with Product, Engineering, and business stakeholders to translate ambiguous business problems into scalable, production\-ready machine learning solutions that directly improve customer experience and business outcomes. You will provide technical direction, mentor Data Scientists, help prioritize the team's work, and help shape the long\-term evolution of Data Science at Rent the Runway.
This is a hands\-on leadership role. You will be expected to remain actively involved in designing, building, and reviewing machine learning solutions while coaching and growing a high\-performing Data Science team.
We're looking for someone with strong technical expertise, product intuition, analytical rigor, leadership skills, and the ability to operate independently in a fast\-paced environment.
What You'll Do:
- Lead, coach, and develop the Data Science team, providing technical guidance, feedback, and career development while fostering a high\-performing, collaborative culture.
- Define the technical direction and priorities of the Data Science function, ensuring work is aligned with business strategy and delivers measurable customer impact.
- Lead strategic data science initiatives by partnering closely with cross\-functional teams to identify, scope, and solve complex business problems.
- Analyze customer, product, and inventory data to identify high\-impact opportunities where machine learning and AI can improve customer and business outcomes.
- Partner closely with Product and Engineering to identify customer problems, shape product strategy, and bring intelligent, AI\-powered customer experiences to life.
- Design, build, deploy, and continuously improve recommendation systems, personalization solutions, and ranking models that enhance customer engagement, conversion, and retention.
- Design and analyze experiments, A/B tests, and causal inference frameworks to measure product and business impact.
- Develop customer similarity models, behavioral segmentation, and ranking approaches to improve personalization throughout the customer journey.
- Partner across Product, Engineering, and the broader Data organization to deliver scalable, production\-ready machine learning solutions.
- Help shape hiring strategy, evaluate talent, and build a high\-performing Data Science organization as the team continues to grow
- Communicate insights, trade\-offs, and recommendations clearly to both technical and non\-technical stakeholders, including executive leadership.
- Drive best practices across the Data \& Analytics organization around experimentation, statistical rigor, model governance, and machine learning development.
About You:
- 7\+ years of hands\-on experience in Data Science, Machine Learning, Applied Statistics, Product Analytics, or a related quantitative field — or equivalent combination of education and experience — including 2\+ years leading Data Science teams or technical initiatives, or demonstrated experience providing technical mentorship and project leadership in a collaborative team environment.
- Bachelor's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Economics, Physics, Operations Research), or equivalent practical experience; advanced degrees and/or specializations are a plus.
- Experience leading, mentoring, or managing Data Scientists or other technical professionals.
- Strong proficiency in Python and experience developing predictive, statistical, or machine learning models.
- Strong proficiency in SQL, with the ability to write efficient and optimized queries against large\-scale datasets.
- Strong foundation in statistics, experimentation, hypothesis testing, and causal inference.
- Proven relevant experience designing, building, and deploying recommendation systems, personalization solutions, ranking models, customer segmentation frameworks, or similar machine learning applications.
- Demonstrated ability to provide technical leadership while balancing hands\-on machine learning work with coaching, mentoring, and people management.
- Strong expertise with cloud AI/ML platforms and services (e.g., GCP, AWS, or Azure), including experience deploying, fine\-tuning, and optimizing managed models. Experience with GCP is a strong plus.
- Experience working with large\-scale datasets in BigQuery, Snowflake, or similar cloud data platforms. Familiarity with dbt and modern analytics engineering practices is a plus.
- Demonstrated ability to independently lead complex projects from problem definition through implementation and stakeholder communication.
- Ability to communicate effectively with a wide range of audiences, including business stakeholders, product managers, engineers, and executive leadership.
- Extremely curious and excited to dive into complex problems.
- Self\-driven, proactive, and comfortable operating with ownership and ambiguity.
Benefits:
At Rent the Runway, we're committed to the wellbeing of our employees, and aim to create a workplace that fosters both personal and professional growth. Our inclusive benefits include, but are not limited to:
- Paid Time Off, including vacation, paid bereavement, and family sick leave \- every employee needs time to take care of themselves and their family.
- Comprehensive health, vision, dental, FSA and dependent care from day 1 of employment \- Your health comes first, and we've got you covered.
- 401(k) match \- an investment in your future.
- Exclusive employee subscription and rental discounts \- to ensure you experience the magic of renting the runway (and give us valued feedback!).
- Universal Paid Parental Leave for both parents \+ flexible return to work program \- because we know your newest family member(s) deserve your undivided attention.
- Paid Sabbatical after 5 years of continuous service \- Unplug, recharge, and have some fun!
- Company\-wide events and outings \- our team spirit is no joke \- we know how to have fun!
- Office\-centric work \- our corporate employees and technical leaders have the option to work remotely on Fridays, in accordance with Company policies.
*Rent the Runway is an equal opportunity employer. In accordance with applicable law, we prohibit discrimination against any applicant or employee based on any legally\-recognized basis, including, but not limited to: race, color, religion, sex (including pregnancy, lactation, childbirth or related medical conditions), sexual orientation, gender identity, age (40 and over), national origin or ancestry, citizenship status, physical or mental disability, genetic information (including testing and characteristics), veteran status, uniformed service member status or any other status protected by federal, state or local law.*
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The anticipated base salary for this position is $160,000 to $195,000\. The actual base salary offered will depend on a variety of factors, including without limitation, the qualifications of the individual applicant for the position, years of relevant experience, level of education attained, certifications or other professional licenses held.
By submitting your application below, you agree that you have read and acknowledge Rent the Runway's Candidate Privacy Policy, found here.
Salary Context
This $160K-$195K 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 Rent the Runway, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($177K) sits 17% below the category median. Disclosed range: $160K to $195K.
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.
Rent the Runway AI Hiring
Rent the Runway has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $195K - $195K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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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