Principal Software Developer

Job Requisition ID #

25WD87569

25WD85265, Principal Software Engineer – AI & ML Platform

About Autodesk

Autodesk makes software for people who make things. We are a global leader in 3D design, engineering, manufacturing, and entertainment software. Our customers use Autodesk software to design and make the physical world that we live in. If you've ever driven a high-performance car, admired a towering skyscraper, used a smartphone, or watched a great film, chances are you've experienced what millions of Autodesk customers are doing with our software.

Position Overview

We are seeking a dynamic and enthusiastic principal software engineer to develop our next-generation AI/ML platform used in the development of Autodesk’s suite of products and services. Join our dynamic and rapidly expanding team to help build innovative capabilities that enable faster and more secure development of machine learning and generative AI solutions, bolstering the intelligence of Autodesk software products and services. You will collaborate with research and product engineering from various domains including design, construction, manufacturing, and media & entertainment to deliver the platform that supports full AI/ML development lifecycle.

Responsibilities

• Innovative System Design: Lead the design and engineering of software systems for the AI/ML Platform, contributing to the full ML development lifecycle.
• Automation and Streamlining: Identify and implement opportunities to automate and streamline ML development processes, fostering efficiency and effectiveness.
• Workflow Automation: Develop comprehensive systems to automate and optimize laborious processes, integrating them seamlessly into our platform to streamline operations.
• ML Solution Deployment: Develop tools for building and deploying ML artifacts in production environments, facilitating a smooth transition from development to deployment.
• Big Data Management: Automate and orchestrate tasks related to managing big data transformation and processing, building large-scale data stores for ML artifacts.
• Scalable Services: Design and implement low-latency, scalable prediction, and inference services to support the diverse needs of our users.
• Cross-Functional Collaboration: Collaborate across diverse teams, including machine learning researchers, developers, product managers, software architects, and operations, fostering a collaborative and cohesive work environment.
• Architectural Leadership: Take ownership of critical components of the platform, providing architectural direction, and contributing to the overall success of the AI/ML Platform.

Minimum Qualifications

• Educational Background: MS in Computer Science, or equivalent practical experience.
• Experience: Over 8 years of experience in software development and engineering, with a solid record of delivering production systems and services.
• Expertise in programming languages such as Python, Java, Go, scripting languages and SQL.
• Demonstrated problem-solving skills with the ability to break down problems into manageable components.
• In-depth experience with Amazon AWS (Amazon Web Services) or Azure cloud technologies.
• Excellent track record in scalable system design and distributed software architecture.
• In-depth experience working with big data technologies, including NoSQL, Hadoop, Spark, Hive, and data pipelines.
• Strong expertise in data platforms, encompassing the design and implementation of scalable and efficient data storage, retrieval, and processing systems.
• Excellent communication and collaboration skills, fostering teamwork and effective information exchange.
• Familiarity with agile development methodologies, including CI/CD & test-driven development.
• Working knowledge with cloud data processing, training, deployment, or operations, such as Snowflake or Databricks.

Preferred Qualifications

• Exposure to deploying ML-enabled projects and solutions to production environments.
• Familiarity with Machine Learning Operations practices.
• Exposure to open-source Large Language Models on Hugging Face like Llama & Mixtral.
• Exposure to ML libraries such as PyTorch, TensorFlow, XGBoost, Pandas, and ScikitLearn.
• Exposure to statistical analysis.
• Past collaboration with data scientists and researchers.