Senior Machine Learning Engineer

Autodesk is a company that transforms how things are made through innovative software solutions. As a Senior Machine Learning Engineer, you will design and develop machine learning systems that enhance customer interactions across various platforms, focusing on conversational AI and intelligent automation.

Responsibilities:

• 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
• Train, adapt, and improve machine learning models, including classical ML models, deep learning models, and LLM-based systems, for real-world production use cases
• Perform statistical analysis and data exploration to generate datasets for model training, experimentation, and evaluation
• Translate business objectives and product requirements into problems that can be addressed using data, statistics, and machine learning
• 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
• Work closely with engineers, MLOps, and product partners to deploy, monitor, and iterate on ML systems running at scale
• Provide technical leadership and mentorship to less experienced team members, supporting their growth and contributing to a strong team culture
• Contribute to improving evaluation practices, ML tooling, and the overall technical foundations of the team

Requirements:

• MS or PhD in Computer Science, Statistics, Engineering, Economics, or related field. We also welcome applicants from non-traditional ML backgrounds
• 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
• 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