Principal Machine Learning Developer

Who you are

• BS or MS in Computer Science, or equivalent practical experience
• Experience: 8+ years of experience in software development and engineering, with a solid record of delivering production systems and services
• Strong background in AI/ML with experience in deep learning, statistical modeling, and neural networks
• Hands-on experience with AI/ML frameworks (such as TensorFlow, PyTorch) and familiarity with the lifecycle of AI/ML model development, from training to deployment
• Strong coding skills in languages commonly used in AI/ML and system development, such as Python, Java, or Go
• Ability to tackle complex technical challenges, analyze potential solutions, and implement the most effective ones
• Strong communication skills to effectively collaborate with cross-functional teams, along with the ability to work independently
• Deep understanding of performance metrics and latency optimization techniques, with the ability to diagnose, tune, and enhance the efficiency of serving systems
• A continuous learning mindset to stay updated with the latest trends and technologies in AI/ML, cloud computing, and software engineering
• Exposure to leveraging GPU computing for AI/ML workloads, including experience with CUDA, OpenCL, or other GPU programming tools, to significantly enhance model training and inference performance
• Experience with big data technologies and ecosystems (Hadoop, Spark, Kafka) for processing and analyzing large datasets in a distributed computing environment
• Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard)
• Experience with HPC techniques and technologies for optimizing computational workloads, particularly in the context of AI/ML model training and inference

What the job involves

• Drive the operational excellence and technical direction of our AI/ML Platform by implementing and optimizing MLOps practices across the full machine learning development lifecycle
• Lead the design and engineering of software systems and platform services for the AI/ML Platform, contributing to scalable, secure, and reliable ML development and operations
• Design and implement automated deployment pipelines for machine learning models and ML artifacts, ensuring seamless transitions from development to production
• Develop comprehensive systems to automate and optimize laborious ML development and operational processes, integrating them into the platform to streamline operations
• Collaborate with cross-functional teams to design, implement, and maintain scalable infrastructure for model training, inference, data processing, and ML artifact management
• Develop tools for building, deploying, and operating ML artifacts in production environments, facilitating a smooth transition from development to deployment
• Automate and orchestrate tasks related to managing large-scale data transformation, data processing, and data stores that support model training, validation, deployment, and operations
• Design and implement low-latency, scalable prediction and inference services to support the diverse needs of platform users and Autodesk product teams
• Develop and maintain robust monitoring and logging systems to track model performance, system health, operational reliability, and overall platform efficiency
• Work closely with data developers to ensure efficient data pipelines for model training, validation, deployment, and ongoing platform operations
• Collaborate across diverse teams, including machine learning researchers, data developers, software developers, product managers, software architects, and operations teams, fostering a collaborative and cohesive work environment
• Implement version control systems for machine learning models and contribute to model governance practices
• Contribute to the implementation of robust model governance practices, version control systems, and adherence to compliance standards. Uphold data privacy and ethical considerations, fostering trust in our AI/ML solutions
• Enforce security best practices and compliance standards in all aspects of MLOps, ensuring data privacy and platform security
• Identify opportunities for process automation and optimization, and implement strategies to enhance the overall MLOps lifecycle
• Take ownership of critical components of the platform, providing architectural direction and contributing to the overall success of the AI/ML Platform