Machine Learning Developer

Job Requisition ID #

25WD87909

Machine Learning Developer - 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 and virtual worlds 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 or played an immersive game, chances are you've experienced what millions of Autodesk customers are doing with our software.

Position Overview

We are seeking a highly skilled Machine Learning Engineer to join the AI/ML Platform team, focusing on the design and development of state-of-the-art tooling to support data scientists and research engineers. The ideal candidate will possess a strong background in software engineering, with a deep understanding of AI/ML technologies, and extensive experience in data science for a variety of use cases. This role demands a strategic thinker who can collaborate effectively with cross-functional teams, drive innovation, and maintain the highest standards of security and usability. As a key contributor to our engineering team, you will play a crucial role in shaping the future of our AI/ML capabilities, delivering solutions that drive significant value for our organization.

Responsibilities

• Dig deep into the data processing pipelines and model training architectures of our customer teams and help inform platform design decisions based on empathy.

• Performance Monitoring and Optimization: Implement monitoring tools and practices to track the performance of AI/ML models during training and in production, identifying waste, bottlenecks, and optimizing system and model performance for better efficiency and reduced costs.

• Model Deployment and Versioning: Oversee the deployment of AI/ML models into production, including the setup of CI/CD pipelines for model deployment and versioning, ensuring smooth and reliable model updates and rollbacks.

• Research and Innovation: Stay abreast of the latest developments in AI/ML technologies, cloud computing, and MLOps practices, exploring and integrating innovative solutions that can enhance the capabilities and efficiency of the AI/ML serving platform.

Minimum Qualifications

• Educational Background: BS or MS in Computer Science, or equivalent practical experience.

• Experience: 5+ 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.

• Expertise in AI/ML Technologies: 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.

• Proficiency in Programming Languages: Strong coding skills in languages commonly used in AI/ML and system development, such as Python, Java, or Go.

• Strong Analytical and Problem-Solving Skills: Ability to tackle complex technical challenges, analyze potential solutions, and implement the most effective ones.

• Excellent Communication and Teamwork Abilities: Strong communication skills to effectively collaborate with cross-functional teams, along with the ability to work independently.

• System Performance Optimization: Deep understanding of performance metrics and latency optimization techniques, with the ability to diagnose, tune, and enhance the efficiency of serving systems.

• Commitment to Continuous Learning: A continuous learning mindset to stay updated with the latest trends and technologies in AI/ML, cloud computing, and software engineering.

Preferred Qualifications

• GPU Computing: 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: Experience with big data technologies and ecosystems (Hadoop, Spark, Kafka) for processing and analyzing large datasets in a distributed computing environment.

• AI/ML Model Monitoring Tools: Familiarity with tools and frameworks for monitoring and managing the performance of AI/ML models in production (e.g., MLflow, Kubeflow, TensorBoard).

• Expertise in High-Performance Computing (HPC): Experience with HPC techniques and technologies for optimizing computational workloads, particularly in the context of AI/ML model training and inference.