Senior Machine Learning and AI Engineer

### Who you are
- Bachelor's degree in Computer Science, Machine Learning, Applied Mathematics, or a related field (or equivalent practical experience)
- 3+ years of experience in Machine Learning, applied AI, or related fields
- Proven experience building and deploying ML systems in production environments
- Strong proficiency in Python and common ML libraries/frameworks such as PyTorch, TensorFlow, scikit-learn, Pandas, or XGBoost
- Experience designing data pipelines and working with large-scale or distributed data systems
- Solid software engineering skills, including experience building APIs and working with distributed systems
- Experience building and deploying systems on AWS (or similar cloud platforms such as GCP or Azure), including familiarity with services such as S3, Lambda, ECS, or Step Functions
- Familiarity with event-driven architectures and tools (CDC, SQS, SNS)
- Strong interpersonal and communication skills, with the ability to collaborate effectively across teams in an agile environment
- Ability to operate independently and drive projects from concept to delivery in ambiguous environments

### What the job involves
- Autodesk is looking for a Senior Machine Learning & AI Engineer to help build the foundation of intelligence-driven user management and access systems within our Admin Access & Insights organization
- This is a greenfield, high-impact role where you will define and deliver machine learning capabilities from the ground up. You will lead the development of intelligent systems that shift our platform from reactive workflows to proactive automation, predictions, and recommendations around user administration and access
- As an early ML hire in this space, you will operate with high ownership - shaping everything from data strategy and pipelines to model development and production systems, working at the intersection of machine learning, platform engineering, and large-scale distributed systems
- Lead the end-to-end development of ML/AI systems, from problem definition and data strategy to model development and production deployment
- Design and build scalable data pipelines by integrating with existing platforms and establishing new data sources where needed
- Develop and deploy machine learning models for prediction, recommendation, and anomaly detection in user management and access workflows
- Build and integrate real-time and batch inference systems into APIs, microservices, and user-facing applications
- Work at the intersection of machine learning, platform engineering, and distributed systems to deliver robust, scalable solutions
- Collaborate closely with platform and experience engineering teams on service architecture, APIs, and event-driven systems
- Establish best practices for ML development, experimentation, deployment, and monitoring
- Drive projects independently in ambiguous environments, taking ownership from concept through delivery
- Collaborate effectively across teams, sharing knowledge and contributing to best practices and high technical standards