Senior Machine Learning Engineer

Autodesk Autodesk · Enterprise · California, USA, GA +3 · Remote

Senior Machine Learning Engineer to design, develop, and evolve machine learning systems for Autodesk's customer-facing platforms, focusing on conversational AI, search, multi-agent solutions, and intelligent automation. The role involves working across the full ML lifecycle, adapting and fine-tuning models, exploring representation learning and ranking, experimenting with agent architectures, and bringing new ML ideas into production. Responsibilities include implementing ML capabilities, training/adapting models (classical ML, deep learning, LLMs), performing statistical analysis, translating business objectives into ML problems, collaborating with cross-functional teams, deploying and monitoring ML systems, and providing technical leadership and mentorship.

What you'd actually do

  1. Design and implement machine learning capabilities that improve Autodesk’s customer-facing platforms, including conversational question answering, search and retrieval, agent-based workflows, and intelligent automation
  2. Train, adapt, and improve machine learning models, including classical ML models, deep learning models, and LLM-based systems, for real-world production use cases
  3. Perform statistical analysis and data exploration to generate datasets for model training, experimentation, and evaluation
  4. Translate business objectives and product requirements into problems that can be addressed using data, statistics, and machine learning
  5. 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

Skills

Required

  • MS or PhD in Computer Science, Statistics, Engineering, Economics, or related field
  • 3+ years of applicable work experience in ML
  • Demonstrated experience applying machine learning techniques, including both classical ML and deep learning approaches, to real-world problems
  • Proficiency with the Python machine learning stack, including tools such as Pandas, NumPy, and Scikit-learn
  • Experience with at least one deep learning framework, such as PyTorch
  • Knowledge of experimental design and analysis, including evaluating model performance and interpreting results
  • Experience or strong interest in NLP, information retrieval, conversational AI, or LLM-based systems
  • Ability to work effectively in cross-functional teams and collaborate with engineers, product partners, and other stakeholders
  • Experience contributing to or supporting machine learning systems in production environments

Nice to have

  • Experience working with Large Language Models, particularly in the context of RAG, conversational systems, question answering, or agent-based applications
  • Exposure to fine-tuning or adapting LLMs or embedding models for domain-specific use cases
  • Experience with information retrieval, learning-to-rank, recommender systems, or other NLP-driven applications
  • Familiarity with search technologies such as OpenSearch, Elasticsearch, Lucene, or Solr
  • Experience with data pipelines, model serving, or MLOps practices, especially in cloud environments such as AWS
  • Advanced software engineering skills, including data structures, algorithms, and building maintainable production code

What the JD emphasized

  • 3+ years of applicable work experience in ML
  • Experience contributing to or supporting machine learning systems in production environments

Other signals

  • LLM-driven conversational platform
  • multi-agent systems
  • intelligent orchestration
  • applied machine learning capabilities
  • conversational question answering
  • retrieval and ranking
  • agent-driven workflows
  • query routing
  • evaluation and measurement
  • continuous improvement of ML-powered systems operating at scale