Senior Applied Ai/ml Developer

Autodesk Autodesk · Enterprise · Ontario, CAN -, Montreal, QC +3 · Remote

Senior ML Developer responsible for designing, analyzing, and delivering data-driven solutions for RAG programs, focusing on user interaction with text outputs and information retrieval. The role involves end-to-end planning, POC building, offline/online evaluation, and deploying ML features into production, with a focus on NLP and ML technology.

What you'd actually do

  1. Design and implement Machine Learning capabilities that improve Autodesk’s RAG platforms
  2. Perform statistical and data analysis and exploration to generate datasets for model training and development
  3. Collaborate with other members of the team to reach better solutions, and to position our team at the cutting edge of technology and ML practice
  4. Provide technical leadership and mentorship for less-experienced members of the team to deliver key ML-powered features for our Digital Customer Platform.
  5. Partner with stakeholders, to solve important business objectives across a range of problems
  6. Translate business requirements and objectives into problems that can be solved with a combination of Data, Statistics, and Machine Learning

Skills

Required

  • MS/M.Tech/M.Math/ or PhD in Computer Science, Statistics, Mathematics, Physics, Developer, Economics, Computational Linguistics or related fields
  • 3+ years of applicable work experience in ML
  • Hands-on experience of NLU and integrating traditional search techniques into RAG
  • Proficiency with the Python Machine Learning stack, e.g. Pandas, deep learning frameworks, traditional ML, probability calibration etc.
  • Knowledge of experimental design and analysis of results
  • Demonstrate experience with leading Machine Learning teams in deploying and improving ML features in production
  • Demonstrate experience working in cross-functional teams to deliver ML solutions in production

What the JD emphasized

  • horizontal expertise
  • vertical domain
  • testing, evaluation and causal inference
  • user interaction with text outputs
  • Information Retrieval
  • end-to-end planning
  • POC-building
  • offline and online evaluation
  • deploying and improving ML features in production
  • cross-functional teams to deliver ML solutions in production

Other signals

  • RAG programs
  • user interaction with text outputs
  • Information Retrieval
  • NLP and ML technology
  • deploying and improving ML features in production