Principal Software Developer- MLOps Platform

Job Requisition ID #

25WD87574, Principal Software Developer- MLOps Platform

Position Overview

We are looking for an experienced Principal Software Engineer to join our platform team focusing on AI/ML Platform (AMP). This team builds and maintains central components to fast track the development of new ML/AI models such as model development studio, feature store, model serving, and model observability. The ideal candidate would have a background in MLOps, Data engineering, and DevOps with experience in building high-scale deployment architectures and observability. As an important contributor to our engineering team, you will help shape the future of our AI/ML capabilities, delivering solutions that inspire value for our organization. You will report to a manager.

Responsibilities

• System design: You will design, implement, and manage software systems for the AI/ML Platform, orchestrating the full ML development lifecycle for the partner teams.
• Mentoring: Spreading your knowledge, sharing best practices, and conducting design reviews to enhance team expertise.
• Multi-cloud architecture: Define components that leverage strengths from multiple cloud platforms (e.g., AWS, Azure) to optimize performance, cost, and scalability.
• AI/ML observability: Build systems for monitoring the performance of AI/ML models and finding insights on the underlying data such as drift detection, data fairness/bias, and anomalies.
• 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.
• End-to-end ownership: Take end-to-end ownership of the components and work with other engineers in the team including design, architecture, implementation, rollout, onboarding support to partner teams, production on-call support, testing/verification, and investigations.

Minimum Qualifications

• Educational Background: Bachelor’s degree in Computer Science or equivalent practical experience.
• Experience: Over 8 years of experience in software development and engineering, delivering production systems and services.
• Prior experience working with MLOps teams at the intersection of expertise across ML model deployments, DevOps, and data engineering.
• Hands-on skills: Ability to fluently translate design into high-quality code in Golang, Python, and Java.
• Knowledge of DevOps practices, containerization, and orchestration tools such as CI/CD, Terraform, Docker, Kubernetes, GitOps.
• Demonstrated knowledge of distributed data processing frameworks, orchestrators, and data lake architectures using technologies such as Spark, Airflow, and iceberg/parquet formats.
• Prior collaborations with Data Science teams to deploy their models, setting up ML observability for inference level monitoring.
• Exposure to building RAG based applications by collaborating with other product teams, Data scientists/AI engineers.
• Demonstrated creative problem-solving skills with the ability to break down problems into manageable components.
• Knowledge of Amazon AWS and/or Azure cloud for solutioning large-scale application deployments.
• Excellent communication and collaboration skills, fostering teamwork and effective information exchange.

Preferred Qualifications

• Experience integrating with third-party vendors.
• Experience in latency optimization with the ability to diagnose, tune, and enhance the efficiency of serving systems.
• Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard).
• Familiarity with distributed model training/inference pipelines using KubeRay or equivalent.
• Exposure to leveraging GPU computing for AI/ML workloads, including experience with CUDA, OpenCL, or other GPU programming tools.
• Exposure to ML libraries such as PyTorch, TensorFlow, XGBoost, Pandas, and ScikitLearn.

Learn More

About Autodesk

Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.

We take great pride in our culture here at Autodesk – our Culture Code is at the core of everything we do. Our values and ways of working help our people thrive and realize their potential, leading to even better outcomes for our customers.

When you’re an Autodesker, you can be your whole, authentic self and do meaningful work that helps build a better future for all. Ready to shape the world and your future? Join us!

Salary Transparency

Salary is one part of Autodesk’s competitive compensation package. Offers are based on the candidate’s experience and geographic location. In addition to base salaries, we also have a significant emphasis on discretionary annual cash bonuses, commissions for sales roles, stock or long-term incentive cash grants, and a comprehensive benefits package.

Diversity & Belonging

We take pride in cultivating a culture of belonging and an equitable workplace where everyone can thrive. Learn more here: Diversity and Belonging.