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About This Role
Chamberlain Group (CG) is a global leader in intelligent access and Blackstone portfolio company. Powered by our myQ technology, we make access simple and secure for millions of homeowners, businesses, and communities worldwide. Our flagship brands, LiftMaster® and Chamberlain® , are found in 51\+ million homes, and 14 million\+ people rely on the myQ® app daily.
This role is a cornerstone in our shift toward AI\-native experiences — moving beyond traditional ML and into systems that reason, act, and collaborate autonomously using foundation models. This role will be responsible for architecting and building LLM\-based agents that integrate with real\-world tools and APIs, orchestrate tasks across roles, and scale intelligently in production environments.
Job Responsibilities:
- Oversee the design, development, and deployment of AI features within Chamberlain products such as: Video\-based search using LLMs, Chatbot interfaces for search and AI\-generated insight, backend pipelines and APIs for AI feature delivery.
- Architect and build agentic AI systems using LLMs, RAG, MCP, tool integration, and orchestration frameworks.
- Design role\-based architectures where agents collaborate through coordination protocols and memory management.
- Build, test, and optimize pipelines using tools such as LangChain, LangGraph, and CAMEL.
- Develop systems for agent reasoning, planning, and long\-term memory retrieval to enable contextual conversations and task execution
- Work across product, engineering, and AI research teams to ensure scalable and performant deployment of assistants
- Drive decision\-making around tooling, infrastructure, observability, and production\-readiness.
- Mentor engineers across the org in AI\-native engineering patterns, architecture, and LLM ecosystem evolution
- Ensure end\-to\-end delivery across frontend, backend, and ML components, and drive code quality, scalability, and maintainability best practices
- Act as the technical voice in roadmap discussions and sprint planning
- Define engineering workflows, coding standards, and agile practices.
- Track and improve delivery velocity, quality metrics, and GenAI tool adoption
- Comply with health and safety guidelines and rules; managers should also ensure compliance across their teams.
- Protect Chamberlain Group’s reputation by keeping information confidential.
- Maintain professional and technical knowledge by attending educational workshops, reading professional publications, establishing personal networks, and participating in professional societies.
- Contribute to the team effort by accomplishing related results and participating on projects as needed.
Job Requirements:
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
- 10\+ years of experience in software engineering, with at least 2\+ years working with LLMs or agentic AI systems.
- Proven experience building LLM\-powered agents that interact with external tools and APIs.
- Demonstrated work with agent orchestration frameworks (e.g., LangGraph, CAMEL, AutoGen).
- Experience leading cross\-functional initiatives in AI productization, intelligent automation, or AI\-native user interfaces.
- Experience in AI infrastructure, prompt engineering, or fine\-tuning LLMs is a strong plus.
- Prior experience working in autonomous systems, virtual assistants, or cognitive agents is highly desirable.
- Video intelligence systems, Computer Vision is a strong plus.
Knowledge, Skills, and Abilities:
- Deep understanding of agentic system design and modern AI system architecture.
- Strong programming skills in Python and experience with production software development.
- Proficiency in LLM\-based development and orchestration tools: LangChain, LangGraph, CAMEL, LlamaIndex, AutoGen, or similar.
- Expertise in agent reasoning, multi\-agent coordination, and role\-based task decomposition.
- Working knowledge of retrieval\-augmented generation (RAG), memory systems, and API\-driven tool use.
- Familiarity with vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding techniques.
- Strong grasp of software architecture, distributed systems, and system integration principles.
- Ability to balance rapid prototyping with scalable production deployment.
- Chatbot frameworks (e.g., LangChain, Rasa, custom pipelines)
- Proficiency with modern backend technologies (Python, Node.js, REST/gRPC APIs).
- Cloud\-native development experience (GCP, AWS, or Azure).
- Experience working with MLOps and model deployment pipelines.
- Excellent communication and people leadership skills.
- Comfortable operating in a fast\-paced, startup\-like environment within an established company
Other:
- Ability to travel up to 10% of the time domestically and internationally
Preferred Job Requirements:
- Master’s degree in Computer Science, Computer Engineering, or related field
- Experience in IoT, software services, video, and AI
- Experience with consumer electronics, smart home companies will be beneficial
The pay range for this position is $133,783\.00 \- $228,241\.50; base pay offered may vary depending on a number of factors including, but not limited to, the position offered, location, education, training, and/or experience. In addition to base pay, also offered is a comprehensive benefits package and 401k contribution (all benefits are subject to eligibility requirements). This position is eligible for participation in a short\-term incentive plan subject to the terms of the applicable plans and policies.
Chamberlain Group wants all of its employees to succeed and encourages people of all backgrounds to apply. We’re proud to be an Equal Opportunity Employer, and you’ll be considered for this role regardless of race, color, religion, sex, national origin, age, sexual orientation, ancestry; marital, disabled or veteran status. We’re committed to fostering an environment where people of all lived experiences feel welcome.
Persons with disabilities who anticipate needing accommodations for any part of the application process may contact, in confidence [email protected].
NOTE: Staffing agencies, headhunters, recruiters, and/or placement agencies, please do not contact our hiring managers directly.
Salary Context
This $133K-$228K range is below the median for AI Software Engineer roles in our dataset (median: $185K across 231 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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Chamberlain Group, 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 $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($181K) sits 17% below the category median. Disclosed range: $133K to $228K.
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.
Chamberlain Group AI Hiring
Chamberlain Group has 2 open AI roles right now. They're hiring across AI Software Engineer. Based in CA, US. Compensation range: $118K - $228K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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).
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 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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