Senior Machine Learning Engineer

About the Role

The Senior Machine Learning Engineer will design, develop, and evolve ML systems that support conversational AI, search, multi‑agent solutions, and intelligent automation on Autodesk’s customer platforms. The role covers the full ML lifecycle—from data exploration and hypothesis formation to modeling, experimentation, deployment, and iteration on production systems used by real customers. Team members collaborate closely with ML engineers, MLOps, product managers, and business stakeholders in a supportive environment that values strong ML fundamentals, thoughtful experimentation, and measurable impact. The group encourages continuous learning, sharing of emerging techniques, and mentorship to drive technical direction at scale. • Design and implement ML capabilities for customer‑facing platforms (conversational QA, search, agent workflows, automation). • Train, adapt, and improve classical, deep learning, and LLM‑based models for real‑world production use. • Conduct statistical analysis and data exploration to create datasets for training, experimentation, and evaluation. • Translate business objectives and product requirements into data‑driven ML problems. • Collaborate with engineers, MLOps, and product partners to deploy, monitor, and iterate ML systems at scale. • Provide technical leadership and mentorship to junior team members, fostering a strong team culture. • Improve evaluation practices, ML tooling, and the team’s technical foundations.

Key Responsibilities

• ▸ml development
• ▸model training
• ▸data exploration
• ▸production deployment
• ▸mlops collaboration
• ▸technical mentorship

What You Bring

• Hold an MS or PhD in a relevant field (or equivalent non‑traditional ML background). • Possess 3+ years of applied machine‑learning experience. • Demonstrated ability to apply both classical ML and deep learning techniques to real problems. • Proficient with the Python ML stack (Pandas, NumPy, Scikit‑learn). • Experience with a deep‑learning framework such as PyTorch. • Knowledge of experimental design, model performance evaluation, and result interpretation. • Interest or experience in NLP, information retrieval, conversational AI, or LLM systems. • Ability to work effectively in cross‑functional teams and collaborate with diverse stakeholders. • Experience supporting ML systems in production environments. • Experience with Large Language Models for retrieval‑augmented generation, conversational or agent‑based applications. • Familiarity with fine‑tuning or adapting LLMs and embedding models for domain‑specific use cases. • Background in information retrieval, learning‑to‑rank, recommender systems, or other NLP‑driven applications. • Knowledge of search technologies such as OpenSearch, Elasticsearch, Lucene, or Solr. • Experience with data pipelines, model serving, and MLOps practices in cloud environments (e.g., AWS). • Advanced software engineering skills, including data structures, algorithms, and maintainable production code.

Requirements

• ▸phd
• ▸python
• ▸pytorch
• ▸mlops
• ▸aws
• ▸nlp

Benefits

Employees receive comprehensive health, financial, wellness, and time‑off benefits, as well as a competitive compensation package. For U.S. roles the base salary ranges from $119,800 to $206,690, with potential bonuses, stock grants, and other incentives. • Comprehensive health, financial, wellness, and time‑off benefits. • Competitive base salary ($119,800–$206,690 U.S.) plus bonuses, stock grants, and additional incentives.

Work Environment

Office Full-Time