Machine Learning Engineer at Autodesk in San Francisco, CA, USA

Summary: Autodesk is a leading company that creates software tools for various industries, influencing creativity and innovation. As a Machine Learning Engineer at Autodesk Research, you will collaborate with researchers and engineers to develop ML-powered product features that enhance customer experiences and contribute to a better world. Responsibilities: - Collaborate on projects at the intersection of research and product with a diverse, global team of researchers and engineers - Write clean, reusable, and well-documented code with proper version control practices - Explore and apply transfer learning strategies to improve model generalization in low-resource settings - Preprocess large-scale datasets and perform feature extraction and analysis to support model development - Design solutions based on error analysis and model performance evaluation - Present results to collaborators, stakeholders and leadership across research and engineering teams - Develop and maintain FastAPI-based backend services to expose ML model inference to internal tools and Autodesk product teams - Monitor and improve model performance in production environments Required Qualifications: - BSc in Computer Science or related fields - At least one internship or equivalent project experience involving machine learning model training and deployment - Hands-on experience developing and deploying deep learning models, including familiarity with model architectures, loss functions, optimization strategies, and regularization techniques - Proficiency with at least one deep learning framework such as PyTorch or TensorFlow - Experience building RESTful backend services or APIs (e.g., FastAPI, Flask) - Familiarity with cloud services and architectures (e.g., AWS, Azure, GCP) - Experience with version control (Git) and writing reproducible, testable code - Good written communication skills for documenting code, architectures, and experiments Preferred Qualifications: - Experience with distributed computing or data processing frameworks (e.g., Ray, Spark) for large-scale dataset preparation - Familiarity with MLOps tooling such as AWS SageMaker, Docker, and Kubernetes for model training and deployment pipelines - Exposure to 2D or 3D geometry data representations, or experience in CAD/engineering software domains - Experience with generative AI models, including LLMs, diffusion models, or multimodal models - Familiarity with cross-domain transfer learning or domain adaptation techniques - Knowledge of the design, manufacturing, or AEC industries - Contributions to open-source ML projects or academic research experience Required Skills: Machine Learning Model Training, Machine Learning Model Deployment, Deep Learning, Deep Learning Frameworks, PyTorch, TensorFlow, RESTful API Development, FastAPI, Flask, Cloud Services, AWS, Azure, GCP, Version Control, Git Benefits: Health and financial benefits, Time away, Everyday wellness, Annual cash bonuses, Commissions for sales roles, Stock grants, A comprehensive benefits package