Interested in this AI/ML Engineer role at Rocket?
Apply Now →About This Role
As a Software Engineer on the Platform Engineering team, you are at the core of how Redfin builds and delivers software. Our team is responsible for the entire lifecycle of a service—from the initial scaffolding and microservices framework to the automated flows that move code through our system. We build the intelligent systems and high\-leverage tooling that sit above our infrastructure, ensuring our teams can ship features with minimal friction and maximum velocity.
We are taking on new areas of focus to build a unified, scalable AI platform for the entire organization. This includes building the infrastructure to leverage AI at scale: an internal AI gateway, a plugin marketplace, and core AI skills. We are looking for an engineer with a strong systems background who can operationalize these tools, ensuring they are secure, cost\-effective, and deeply integrated into our existing workflows.
About the Role
- Maintain \& Modernize Developer Flows: Own and evolve the core systems that Redfin engineers use daily, including our Spring\-based microservices framework, service scaffolding, and code delivery pipelines.
- Architect the AI Platform Layer: Build and scale the "plumbing" for AI adoption, including an internal gateway to manage various providers and a plugin marketplace for custom workflows.
- Operationalize AI Tooling: Lead the technical implementation of cost governance, security guardrails, and usage monitoring for all AI tools integrated into the Redfin ecosystem.
- Integrate Intelligence into the Lifecycle: Identify and implement ways to bake AI\-driven tools into existing developer workflows, automating repetitive tasks and accelerating the path to production.
- Shepherd Engineering AI Adoption: Partner with engineering teams to help them onboard and use AI tools safely. You’ll serve as the technical guide to ensure their workflows are efficient, secure, and sustainable within our platform.
About You
- Strong Systems Background: You have a deep understanding of Java or a similar strongly\-typed language (Spring/Spring Boot experience is a major plus).
- Practical AI Tooling Knowledge: You have experience integrating AI tools or APIs into your projects (professional or personal). You are familiar with the technical side of making these tools work, such as managing API calls, understanding rate limits, and the basic security considerations of using third\-party AI services.
- Systems \& Tooling Background: A track record of building frameworks, CLI tools, or automated workflows that support large\-scale engineering operations.
- Operational Excellence: You have experience managing the "care and feeding" of technical platforms—focusing on reliability, cost optimization, and performance.
- Reliability Mindset: You take pride in the long\-term health of the tools you build. You enjoy solving complex problems in existing codebases just as much as building new features.
- Marketplace \& Gateway Design: Experience building internal developer portals, service catalogs, or API management layers.
- Internal Product Mindset: Experience treating a platform like a product, focusing on documentation, usability, and helping others adopt new tools.
- Security \& Compliance: Familiarity with ensuring third\-party tools and data flows adhere to corporate standards for privacy.
Whatyou’llget
Our team members fuel our strategy, innovation and growth, so we ensure the health and well\-being of not just you, but your family, too! We go above and beyond to give you the support you need on an individual level and offer all sorts of ways to help you live your best life. We are proud to offer eligible team members perks and health benefits that will help you have peace of mind. Simply put: We’ve got your back. Check out our full list of Benefits and Perks.
On\-Call Expectations
This role may include participation in an on\-call rotation to support production systems and ensure service reliability. On\-call responsibilities may include coverage during nights and weekends. If applicable, frequency and scheduling will be determined by team needs and communicated accordingly.
Aboutus
Redfinis a technology\-driven real estate company with the country's most\-visited real estate brokerage website. As part of Rocket Companies (NYSE: RKT), Redfin is creating an integrated homeownership platform from search to close to make the dream of homeownership more affordable and accessible for everyone. Redfin’s clients can see homes first with on\-demand tours, easily apply for a home loan with Rocket Mortgage, and save thousands in fees while working with a top local agent.
*This job description is an outline of the primary responsibilities of this position and may bemodifiedat the discretion of thecompany at any time. Decisions related to employment are not based on race, color, religion, national origin, sex, physical or mental disability, sexual orientation, gender identity or expression, age, military or veteran status or any other characteristic protected by state or federal law. Thecompany provides reasonableaccommodationsto qualified individuals with disabilitiesin accordance withapplicable state and federal laws. Applicantsrequiringreasonable accommodations in completing the application and/orparticipatingin the application process should contact a member of the Human Resources team, at*[email protected]*.*
*The compensation information below is provided in compliance with all applicable job posting disclosure requirements. The compensation for this position is$166,900\.00\-$203,900\.00.The position may also be eligible for an annual bonus, incentives, and other employment\-related benefits including, but not limited to, medical, dental, and vision benefits, 401K retirement plan, and paid\-time off. More informationregardingthese benefits and others can be found*here*. The informationregardingcompensation and other benefits included in this paragraph is the company’s current, good faithestimateat the time of posting. \[Compensation and benefits are subject to modification from time to time as the Company, in its sole and exclusive discretion,deemsappropriate.] The Company maydetermineduring its future reviews of the proposed compensation and benefits provided for this position, that the compensation and benefits for suchpositionshould be reduced. In no event will the Company reduce the compensation for the position to a level below the applicable jurisdictional minimum wage rate for the position. Los Angeles County and San Francisco Candidates only: qualified applicants with arrest or conviction records will be considered for employment per the Fair Chance Ordinance and the Fair Chance Initiative for Hiring.*
Salary Context
This $166K-$203K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rocket, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($185K) sits 15% below the category median. Disclosed range: $166K to $203K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rocket AI Hiring
Rocket has 4 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Detroit, MI, US, MI, US, Seattle, WA, US. Compensation range: $203K - $276K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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