Senior Applied Scientist, Personalization & Agentic Systems

Autodesk Autodesk · Enterprise · Toronto, ON +1

Senior Applied Scientist role focused on designing, building, and productionizing agent-driven personalization platforms using LLMs and agentic systems for Autodesk products. The role involves developing ML models, agentic frameworks, inference services, and data pipelines, with a strong emphasis on operational excellence and delivering AI/ML systems into scalable production environments.

What you'd actually do

  1. Design, build, and operate production-grade agentic personalization systems that adapt to user behavior and context
  2. Research, develop and implement ML models using Transformers, LLMs, and classical ML algorithms for search and recommendation use cases
  3. Be proficient in agentic frameworks (e.g., LangGraph) and building systems that orchestrate multi-step reasoning, tool use, and decision-making workflows.
  4. Be proficient in optimized inference engines/frameworks like vLLM, TensorRT
  5. Productionize ML- and LLM-powered workflows, including inference services, orchestration, monitoring, and iteration

Skills

Required

  • 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

Nice to have

  • Experience mentoring Junior engineers or acting as a technical lead on complex initiatives
  • Exposure to AI governance, privacy, or safety considerations at scale

What the JD emphasized

  • production-grade agentic personalization systems
  • agentic frameworks
  • optimized inference engines/frameworks
  • Productionize ML- and LLM-powered workflows
  • agentic systems that leverage agent memory, retrieval-augmented generation (RAG), and tool ecosystems/APIs
  • evaluate LLM performance

Other signals

  • production-grade agentic personalization systems
  • ML models using Transformers, LLMs
  • agentic frameworks (e.g., LangGraph)
  • optimized inference engines/frameworks like vLLM, TensorRT
  • Productionize ML- and LLM-powered workflows
  • agentic systems that leverage agent memory, retrieval-augmented generation (RAG), and tool ecosystems/APIs
  • evaluate LLM performance