Position Overview 

 Do you thrive in a fast-paced, high-energy environment? Are you a creative problem solver who enjoys building solutions and products using cutting edge technologies? Are you looking to collaborate with motivated individuals from diverse backgrounds? Do you have the drive to make things happen? If so, you are in the right place.  

Autodesk is leading the transformation of how users interact with design and engineering software by embedding AI deeply into our products. We are building cloud-native, AI-powered platforms that operate at scale, leveraging data, machine learning, and agentic systems to deliver intelligent, adaptive, and personalized experiences across our flagship products including AutoCAD, Revit, Construction Cloud, and Forma. 

The Personalized Experiences Team is a centralized Personalization group working closely with product line development teams across the company to democratize ML/Analytics across all Autodesk products. As a Senior Applied Scientist, Personalization, you will play a key role in designing, building, and productionizing agent-driven personalization platforms used by millions of customers. This role is ideal for a senior, hands-on engineer who combines strong software fundamentals with experience delivering AI and ML systems into reliable, scalable production environments. 

As an Applied Scientist at Autodesk, you will operate at the intersection of research and engineering building AI agents, ML systems, data platforms, and helping transform advanced AI capabilities into trusted, reproducible, and widely adopted platform features.  This position is remote Canada.

Responsibilities 

• Design, build, and operate production-grade agentic personalization systems that adapt to user behavior and context 

• Research, develop and implement ML models using Transformers, LLMs, and classical ML algorithms for search and recommendation use cases 

• Be proficient in agentic frameworks (e.g., LangGraph) and building systems that orchestrate multi-step reasoning, tool use, and decision-making workflows. 

• Be proficient in optimized inference engines/frameworks like vLLM, TensorRT 

• Productionize ML- and LLM-powered workflows, including inference services, orchestration, monitoring, and iteration 

• Build agentic systems that leverage agent memory, retrieval-augmented generation (RAG), and tool ecosystems/APIs to deliver context-aware personalization and recommendations 

• Build and evolve data and feature pipelines that power personalization, experimentation, and feedback loops 

• Develop robust methods to evaluate LLM performance, ensuring ethical AI practices 

• Monitor and improve deployed models based on real-world performance metrics and customer feedback 

• Collaborate with cross-functional teams including engineering, product management, and operations to translate research into production systems 

Minimum Qualifications 

• BS or MS in Computer Science, Engineering, or a related field 

• 6 or more years of professional software engineering experience, including ownership of production systems 

• Strong experience building AI systems in cloud environments, AWS preferred 

• Hands-on experience delivering AI, ML, or LLM-powered systems into production 

• Experience with AI agents, orchestration frameworks, or hybrid reasoning systems 

• Experience with MLOps practices, experimentation frameworks, and model monitoring 

• Proficiency in one or more programming languages such as Python, Java, or equivalent 

• Experience working with data systems including RDBMS, NoSQL, data warehouses, and streaming platforms 

• Strong understanding of system design, scalability, and operational excellence 

• Ability to work autonomously while collaborating effectively across engineering, product, and data teams 

• Excellent communication skills and ability to influence technical decisions 

• Experience building personalization, recommendation, or insight platforms 

• Experience with real-time or event-driven architectures 

• Exposure to AI governance, privacy, or safety considerations at scale 

• Experience mentoring Junior engineers or acting as a technical lead on complex initiatives