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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 central to our mission of enhancing everyday intelligence in connected systems. This role will be responsible for building robust pipelines that analyze temporal behavior, identify anomalies, and trigger intelligent alerts.
Job Responsibilities:
- Design, develop, and deploy machine learning models for detecting anomalies in time-series data from connected devices (e.g., cameras, door sensors, access logs).
- Build pipelines that analyze historical patterns and identify deviations using a combination of statistical, classical ML, and deep learning approaches.
- Collaborate with data engineering and platform teams to ensure real-time model inference, stream processing, and alert triggering
- Tune models for low false positives and high sensitivity while maintaining operational efficiency.
- Develop and manage ML Ops pipelines for training, testing, deployment, and monitoring of anomaly detection systems
- Implement robust monitoring and feedback loops to adapt to behavior drift or seasonal variations.
- Contribute to overall AI system architecture and collaborate with engineers working on LLM-based assistants and agentic AI to ensure seamless integration.
- Work across product, engineering, and AI research teams to ensure scalable and performant deployment of models
- 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
- Manage, mentor, and grow a team of applied AI and full-stack engineers.
- Guide career development. Hire top talent and build a strong, collaborative team culture.
- 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
- 10+ years of experience software engineering experience with 5+ in ML engineering or data science with a focus on real-time systems, behavior modeling, or time-series data.
- Proven experience building and deploying anomaly detection systems in production.
- Hands-on experience with time-series libraries (e.g., Prophet, Darts, GluonTS, Kats, Merlion).
- Experience working with event-driven architectures, edge devices, or smart home / IoT environments is a plus.
- Familiarity with vector databases or integration with LLMs
Knowledge, Skills, and Abilities:
- Strong understanding of time-series analysis, including: Trend/seasonality decomposition, Statistical anomaly detection methods (ARIMA, z-score, moving average), Classical ML techniques (Isolation Forest, One-Class SVM, clustering-based approaches), Deep learning for sequences (LSTM, GRU, TCN, Transformers)
- Experience in streaming data processing and building pipelines that operate on live or near-real-time data.
- Knowledge of change point detection, event modeling, and alert systems.
- Ability to design interpretable anomaly detection systems with threshold tuning, confidence scoring, and alert escalation.
- Understanding of MLOps principles, including CI/CD for ML, versioning, and retraining strategies.
- Strong programming skills in Python and familiarity with ML frameworks (scikit-learn, PyTorch, TensorFlow).
- Proficient in data handling and engineering tools such as Pandas, NumPy, Kafka, Spark, or Flink
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
- Analytical and systems thinker who can reason about behavioral patterns over time
- Detail-oriented with a strong focus on data integrity and alert quality
- Comfortable operating in a fast-paced environment and building 0-1 systems
- Effective communicator — able to explain models, metrics, and uncertainty clearly to technical and non-technical stakeholders
- Experience with consumer electronics, smart home companies will be beneficial
The pay range for this position is $148,912.00 - $254,054.00; 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 $148K-$254K range is above the median for AI Software Engineer roles in our dataset (median: $194K across 269 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 11,520 AI roles we're tracking, AI Software Engineer positions make up 5% 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 $220,000 based on 1,688 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($201K) sits 8% below the category median. Disclosed range: $148K to $254K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Chamberlain Group AI Hiring
Chamberlain Group has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Oak Brook, IL, CA, US. Compensation range: $215K - $254K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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