Applied AI Developer, Autodesk

• 1 to 2 years of experience in software engineering, ML engineering, or related fields, including internships, co-ops, or full-time industry experience
• Strong programming fundamentals and experience in at least one of C++ or TypeScript for integration points with desktop or web products
• Exposure to applied ML concepts and workflows, including model evaluation, experimentation, and deploying or integrating ML components into software systems
• Familiarity with integrating and evaluating third-party models or LLMs, including basic prompt engineering and an understanding of RAG patterns and safety considerations
• Interest in building low-latency systems and learning how to integrate ML into product surfaces for real-time inference
• Ability to reason about software quality, performance, reliability and security, and to apply best practices with support from mentors
• Clear communication skills and ability to collaborate with product and engineering stakeholders as part of a cross-functional team
• Bachelor’s degree in Computer Science, Engineering or related field, or equivalent experience

Desirable

• We are hiring an Applied AI engineer to help build and operationalize applied AI and ML systems across Autodesk products with a focus on the Fusion Industry Cloud
• The Applied AI Engineer will contribute to the delivery of ML initiatives, including applied AI capabilities such as Agent Integration and MCP tools deployment
• This role will partner closely with Product, Engineering and the Applied AI manager to help turn ML investments into reliable, production-grade capabilities and measurable product outcomes
• The role requires strong engineering fundamentals, curiosity, and the ability to learn quickly while collaborating across teams
• Contribute to the design and delivery of applied AI systems that are production-ready, reliable and maintainable, with guidance from senior engineers
• Implement and improve components supporting agent integration, agent stores and observability of agent performance
• Assist in evaluating and prototyping model approaches, including LLM integrations, retrieval-augmented generation, hybrid model architectures and agentic components
• Follow established standards for agent and model governance, responsible AI, bias detection, explainability and data privacy
• Participate in code reviews, technical design reviews and team discussions; incorporate feedback to improve engineering quality and maintainability
• Collaborate with product and UX partners to translate product goals into technical tasks, success metrics and experiment plans
• Document technical decisions, implementation details and learnings, and communicate progress and risks to the team