Senior Software Engineer, Model Training
Baseten
AWSAzureCloudDistributed SystemsGoogle Cloud PlatformKubernetesPyTorchRaySpark
Senior/Principal Machine Learning Engineer, Generative AI
Autodesk
full-time
Posted on:
Origin: • 🇺🇸 United States • Colorado
Visit company websiteSalary
💰 $166,600 - $269,500 per year
Job Level
Senior
Tech Stack
AWSCloudDistributed SystemsPyTorchRaySpark
About the role
- Report to an ML Development Manager for the Generative AI team
- Join the AEC Solutions team to build cutting-edge foundation models and generative AI tools for the AEC industry
- Collaborate across organizations with AI Researchers, ML Engineers, Software Architects, and Experience Designers to generate and interpret design data
- Set the strategic technical vision for Autodesk’s generative AI capabilities in the AEC domain
- Lead the design and development of intelligent data processing and characterization systems that transform unstructured inputs into structured, ML-ready formats
- Architect and implement scalable, production-grade data and ML pipelines that support training and fine-tuning of models
- Drive strategic technical planning—identify bottlenecks, propose architectural improvements, and align data/ML infrastructure with product goals
- Collaborate closely with data engineers, applied scientists, and product teams to integrate large-scale data into model development workflows
- Perform hands-on development of data preprocessing, feature extraction, and transformation modules optimized for downstream ML model performance
- Define and establish best practices for model experimentation, evaluation, and deployment in high-throughput environments
- Investigate and apply advanced techniques including self-supervised learning, active learning, and weak supervision to maximize the value of unlabeled data
- Own and evolve the model/data feedback loop by monitoring model quality, diagnosing failure modes, and guiding iterative improvements
- Mentor and support a team of ML engineers, fostering a culture of engineering excellence and technical ownership
- Stay current with advances in generative AI, foundation models, and data-centric AI—translating research into practical, scalable solutions
Requirements
- A Master's degree (or higher) in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Statistics or a related field
- 10+ years of work experience in machine learning, data science, AI, or a related field with a proven track record of technical leadership and hands-on implementation
- Deep understanding of data modelling, system architectures, and processing techniques, including 2D/3D geometric data representations
- Expertise in deep learning architectures (e.g., Transformers, CNNs, GANs) and modern ML frameworks (e.g., PyTorch, Lightning, Ray)
- Experience with Large Models (LLMs and/or VLMs) and related technologies, including frameworks, embedding models, vector databases, and Retrieval-Augmented Generation (RAG) systems, in production settings
- Experience with AWS cloud services and SageMaker Studio for scalable data processing and model development
- Strong foundation in computer science fundamentals, distributed computing, and algorithmic efficiency
- Proven ability to translate theoretical concepts into practical solutions and prototype implementations
- Ability to work autonomously while effectively collaborating across teams, bridging the gap between research and practical implementation
- Excellent technical writing and communication skills for documentation, presentations, and influencing cross-functional teams
- Background in Architecture, Engineering, or Construction (preferred)
- Extensive experience in system design for data preparation, hyperparameter selection, acceleration techniques, and optimization methods
- Proficiency in parallel and distributed computing techniques, with hands-on experience using platforms like Spark, Ray, or similar distributed systems for large-scale data processing and model training
- Familiarity with responsible AI principles, including bias mitigation, explainability, and ethical AI practices
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