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About This Role
As the Engineering Lead, you will lead the team building a newly launched AI\-first product with direct revenue impact. Our platform analyzes client communications and engagement signals to help mortgage bankers, the sales professionals who guide clients through home financing, focus on the clients most likely to convert. Your team builds agentic AI applications: systems where agents reason, plan, use tools, and act on their own to drive real business outcomes.
This is a player\-coach role. You'll spend real time in the code: shaping architecture, reviewing designs, building critical pieces yourself, and setting the technical bar by example. At the same time, you'll own the health and direction of the team: hiring, growing engineers, setting priorities with product partners, and making sure the team ships things that matter. AI is not a side tool here. You'll use it as a core part of how you and the team design, code, test, debug, review, and ship software.
The ideal candidate is a strong engineer who genuinely wants to lead: someone energized by building a team and a product at the same time, and by defining what AI\-native engineering looks like in practice.
This is a rare setup. You get the autonomy, pace, and greenfield ownership of an early\-stage startup, backed by the stability, resources, and built\-in distribution of an established company. The product just launched, usage is growing, and both the architecture and the team are still being shaped. You won't inherit someone else's team or someone else's technical decisions. You'll build both, and your choices will define how this product and this team work for years. You'll also get access to frontier AI development tools, including AI coding agents like Claude Code, as a core and funded part of how the team works.
About therole
- Lead a team of engineers building agentic AI capabilities such as agent orchestration, tool calling, retrieval\-augmented workflows, and LLM\-powered features.
- Stay hands\-on: contribute to architecture, write and review code, and build critical components alongside the team.
- Own the technical strategy for the platform, balancing speed, scalability, maintainability, and business impact.
- Set the standard for evaluation, observability, and guardrails for AI agents, ensuring reliability, safety, and measurable quality in production.
- Hire, coach, and grow engineers, giving clear feedback and creating real growth paths for the team.
- Partner with product, design, data, and business stakeholders to set priorities and translate ambiguous business needs into a clear roadmap.
- Define how the team works: engineering practices, AI\-native development workflows, code review culture, and delivery cadence.
- Lead collaboration across distributed engineering teams, including partners in India.
Aboutyou
You're an engineer first and a leader by choice, not by escape from the code. You've shipped real systems, you still love building, and you've discovered that growing a team multiplies your impact more than any individual contribution.
You're serious about AI, both in the product and in how the team works, and you want to lead a team that sets the example for AI\-native engineering. You're comfortable in an early\-stage environment where requirements evolve and priorities shift, and you bring clarity to the team rather than pass ambiguity down.
Minimum Qualifications
- 8\+ years of professional software development experience, including 2\+ years leading engineers as a manager or tech lead.
- Strong hands\-on experience building production software in languages such as C\#, Java, Python, JavaScript, or TypeScript.
- Experience designing and operating full\-stack systems across frontend, backend, APIs, and data integrations on cloud platforms such as AWS, Azure, or Google Cloud.
- Track record of hiring, coaching, and growing engineers.
- Experience owning technical strategy or architecture for a product or platform.
- Demonstrated ability to work with product and business stakeholders to set priorities and deliver outcomes.
Preferred Qualifications
- Hands\-on experience building with agentic AI frameworks (e.g., LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar), LLM APIs, or Model Context Protocol (MCP) tool integrations.
- Experience with LLM application patterns such as retrieval\-augmented generation (RAG), agent evaluation harnesses, guardrails, or production monitoring of AI behavior.
- Experience leading a team through AI\-native ways of working, such as AI coding agents, AI\-assisted reviews, or multi\-agent workflows.
- Experience building or leading a team in a greenfield or newly formed product environment.
- Experience with AWS, Kubernetes, CI/CD, automated testing, and production operations.
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.
Aboutus
Rocket is a Detroit\-based company made up of businesses that provide simple, fast and trusted digital solutions for complex transactions. The name comes from our flagship business, now known as Rocket Mortgage®, which was founded in 1985\. Today, we’re a publicly traded company involved in many different industries, including mortgages, fintech, real estate and more. We’re insistently different in how we look at the world and are committed to an inclusive workplace where every voice is heard. *Apply today to join a team that offers career growth, amazingbenefitsand the chance to work with leading industry professionals.*
*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*Careers@Rocket.com*.*
*The compensation information below is provided in compliance with all applicable job posting disclosure requirements. The compensation for this position is* *$123,500\.00\-$276,000\.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 information regarding these benefits and others can be found* *here****. The information regarding compensation and other benefits included in this paragraph is the company’s current, good faith estimate at the time of posting. \[Compensation and benefits are subject to modification from time to time as the Company, in its sole and exclusive discretion, deems appropriate.] The Company may determine during its future reviews of the proposed compensation and benefits provided for this position, that the compensation and benefits for such position should 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 $123K-$276K range is above the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Rocket, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($199K) sits 9% below the category median. Disclosed range: $123K to $276K.
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
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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