Senior NLP & RAG ML Engineer

Autodesk; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Autodesk is seeking a Sr. Applied AI/ML Developer to lead design, analysis, and delivery of data-driven ML solutions across RAG programs. You will work hands-on with NLP, ML techniques, and information retrieval to build, test, and deploy features in a collaborative, agile environment.

You will guide less-experienced teammates, coordinate with stakeholders, and translate business needs into ML problems. Strong emphasis on experimental design and scalable ML delivery.

Qualifications

• MS/Tech/Math or PhD in a related field
• 3+ years of ML experience
• Hands-on experience with NLU and integrating traditional search into RAG
• Proficiency with Python ML stack (Pandas, DL frameworks, ML, calibration)
• Knowledge of experimental design and result analysis
• Experience leading ML teams in production
• Experience working in cross-functional teams to deploy ML solutions

Responsibilities

• Design and implement ML capabilities improving Autodesk's RAG platforms
• Perform statistical analysis to generate datasets for training
• Collaborate with team to push features to production
• Provide technical leadership and mentorship for ML features
• Partner with stakeholders to align with business objectives
• Translate business requirements into ML/data problems

Skills

NLU

Experiment design

Leadership in ML teams

Cross-functional collaboration

Statistical analysis

Education

MS/PhD in CS/Statistics/Math/Physics/Economics/Computational Linguistics

Tools

Python ML stack

Pandas

Deep learning frameworks

Job description

Autodesk is seeking a Sr. Applied AI/ML Developer to lead design, analysis, and delivery of data-driven ML solutions across RAG programs. You will work hands-on with NLP, ML techniques, and information retrieval to build, test, and deploy features in a collaborative, agile environment.

You will guide less-experienced teammates, coordinate with stakeholders, and translate business needs into ML problems. Strong emphasis on experimental design and scalable ML delivery.