Job Requisition ID #
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 new ML/AI models such as model development studio, feature store, model serving, model observability. Ideal candidate would have background in ML Ops, Data engineering, DevOps with the experience of building high scale deployment architectures, 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, doing design reviews to step up the expertise at the team level
Multi-cloud architecture: Define components which leverages strengths from multiple cloud platforms (e.g., AWS, Azure) to optimize performance, cost, and scalability
AI/ML observability: You will build systems for monitoring performance of AI/ML models and finding insights on the underlying data such as drift detection, data fairness/bias, anomalies
ML Solution Deployment: You will 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: You will take the 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, investigations etc
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 of working with MLOps team at the intersection of the expertise across ML model deployments, DevOps, data engineering
Hands-on skills: Ability to fluently translate the design into high quality code in golang, python, Java
Knowledge of DevOps practices, containerization, 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, iceberg/ parquet formats
Prior collaborations with Data science teams to deploy their models, setting up ML observability for inference level monitoring
Exposure for 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 of 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, to significantly enhance model training and inference performance
Exposure to ML libraries such as PyTorch, TensorFlow, XGBoost, Pandas, and ScikitLearn
#LI-AC3
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, which leads 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
Diversity & Belonging
We take pride in cultivating a culture of belonging and an equitable workplace where everyone can thrive. Learn more here: https://www.autodesk.com/company/diversity-and-belonging
Are you an existing contractor or consultant with Autodesk?
Please search for open jobs and apply internally (not on this external site).