Postdoctoral In Reinforcement Learning Jobs in Arizona

Honeywell is seeking a Sr. Advanced AI Software Engineer to join our team! In this role, you will lead the strategic vision and roadmap while driving performance, reliability, and quality across our software products to support scalable business growth.

You will report directly to our Director of Engineering, and you'll work out of our Phoenix, Arizona location on a hybrid work schedule. Note: for the first 90 days, new hires must be prepared to work onsite 100% M-F.

YOU MUST HAVE

• 5+ years of hands-on AI/ML experience in production environments. Proven track record of delivering AI-powered systems at scale. Experience working in cross-functional, fast-paced environments
• Core AI / Machine Learning
• Deep expertise in: Machine learning fundamentals (supervised, unsupervised, reinforcement learning). Deep learning architectures (CNNs, RNNs, Transformers)
• Hands-on experience with:Large Language Models (LLMs) and generative AI, Prompt engineering, fine-tuning, and RAG pipelines. Embeddings, vector databases, and semantic search.
• Programming & Software Engineering: Strong proficiency in Python (primary AI development language), Experience with at least one additional language (Java, C++).
• Solid understanding of:Data structures, algorithms, and distributed systems, API design, microservices, and backend integration, Ability to write clean, maintainable, production-quality code, AI & ML Frameworks.
• Strong experience on one or more cloud platforms:Azure, AWS, Containerization and orchestration:Docker, Kubernetes: CI/CD tools for ML workflows

WE VALUE

• Bachelor's degree in Computer Science, AI, ML, Data Science, or a related field
• Master's degree (PhD is a Plus) in Computer Science, AI, ML, Data Science, or a related field
• Multi-agent systems and autonomous agents
• Edge AI or model compression/quantization
• AI safety research and explainability techniques
• Knowledge of: SQL and NoSQL databases
• Familiarity with vector databases
• Publications, patents, or open-source contributions in AI/ML

US PERSON REQUIREMENT

Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person, which is defined as a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status, or have the ability to obtain an export authorization.

BENEFITS OF WORKING FOR HONEYWELL
In addition to a competitive salary, leading-edge work, and developing solutions side-by-side with dedicated experts in their fields, Honeywell employees are eligible for a comprehensive benefits package. This package includes employer-subsidized Medical, Dental, Vision, and Life Insurance; Short-Term and Long-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays.

ABOUT HONEYWELL
Honeywell International Inc. (Nasdaq: HON) invents and commercializes technologies that address some of the world's most critical challenges around energy, safety, security, air travel, productivity, and global urbanization. We are a leading software-industrial company committed to introducing state-of-the-art technology solutions to improve efficiency, productivity, sustainability, and safety in high-growth businesses in broad-based, attractive industrial end markets. Our products and solutions enable a safer, more comfortable, and more productive world, enhancing the quality of life of people around the globe.

The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates. Posting date: 4/28/2026

• Design, build, and deploy advanced AI/ML systems (including LLMs and generative AI) end to end.
• Architect scalable, reliable, and cost-efficient AI solutions for production environments.
• Lead model optimization, evaluation, monitoring, and continuous improvement.
• Drive MLOps practices for training, deployment, CI/CD, and lifecycle management.
• Collaborate with product, data, and platform teams to deliver business-aligned AI outcomes.
• Provide technical leadership through design reviews, mentorship, and AI strategy influence.