Job Requisition ID #
26WD101236
Position Overview
We are looking for an exceptional Machine Learning Engineer to design, build, operationalize, and scale production-grade AI/ML and Agentic AI systems at Autodesk For Go-to-market intelligence function.
The mission of the team is to empower decision makers and the broader data communities through trusted data assets and scalable self-serve intelligence. The focus of this role will be engineering end-to-end AI/ML solutions - including feature engineering, data cleansing, contributing in model training process, model deployment, model validations, model evaluation, inference pipelines, and production orchestration.
You will work at the intersection of machine learning, data & analytics engineering, You will collaborate closely with data engineers, data scientists, analysts, platform teams, and business stakeholders to deliver reusable intelligent data products at enterprise scale.
The role requires a strong engineering mindset, hands-on experience building production ML systems, and the ability to evaluate and integrate rapidly evolving AI technologies while maintaining high standards for quality, observability, security, governance, cost efficiency, and operational reliability.
Responsibilities
- Design, develop, test, deploy, and maintain production-grade ML pipelines supporting enterprise-scale use cases
- Develop reusable ML services and components
- Develop robust model evaluation frameworks covering dimensions such as accuracy, relevance, groundedness, consistency, latency, throughput, robustness, and cost
- Design automated evaluation pipelines using deterministic metrics, model-based evaluation, curated datasets, regression testing, and human evaluation where appropriate
- Build and maintain distributed processing pipelines capable of handling large volumes of documents, web content, structured data, and unstructured data efficiently
- Design and optimize distributed pipeline and cloud orchestration for large-scale AI workloads using appropriate workflow orchestration and cloud-native technologies
- Implement resilient processing patterns including concurrency management, queue-based architectures, checkpointing, retries, failure recovery, rate limiting, and idempotent processing
- Optimize AI/ML systems for latency, throughput, scalability infrastructure utilization, and model inference cost
- Partner with platform engineering teams to integrate AI applications with the relevant platforms, APIs, identity and access management, monitoring, and deployment infrastructure
- Implement appropriate MLOps and LLMOps practices, including model and prompt versioning, experiment tracking, evaluation, deployment automation, monitoring, rollback mechanisms, and lifecycle management
- Build comprehensive observability and monitoring mechanisms across ML pipelines, covering pipeline health, model performance, data quality, failures, and cost
- Implement mechanisms to identify and manage model drift, data drift, quality degradation, and upstream data changes
- Build modular frameworks and reusable components that enable teams to develop new capabilities through self-service patterns rather than one-off implementations
- Work closely with data scientists, data engineers, analysts, product teams, and business stakeholders to translate business problems into appropriate ML architectures and implementation strategies
- Translate complex ML system designs, model behavior, limitations, and trade-offs into business-appropriate representations for technical and non-technical stakeholders
- Support experimentation and rapid prototyping while ensuring successful solutions can transition into maintainable, production-grade systems
- Contribute to engineering standards, reference architectures, design reviews, code reviews, technical documentation, and AI/ML engineering best practices
Minimum Qualifications
- Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Information Systems, or a related technical discipline
- 5+ years of machine learning engineering, or data engineering, or related experience, including significant experience developing production systems
- Demonstrated experience designing and operating production ML systems rather than only experimentation or notebook-based model development
- Strong programming skills in Python, with the ability to develop modular, testable, maintainable, and production-quality software
- Working experience with Snowflake, Hands-on experience with Snowflake utilities, Snow SQL, Snow Pipe. Must have worked on Snowflake Cost optimization scenarios
- Experience with workflow orchestration technologies such as Airflow or comparable orchestration frameworks
- Have experience on Data transformation tools like DBT
- Hands-on experience building and deploying machine learning inference pipelines and services
- Experience designing distributed data or ML processing pipelines for high-volume workloads
- Experience deploying workloads into a major cloud environment, preferably AWS, and working with cloud services for compute, storage, event processing, monitoring, and distributed execution
- Experience with Git-based software development workflows, code reviews, branching strategies, and collaborative engineering practices
- Familiarity with MLOps concepts, including experiment tracking, model lifecycle management, deployment, model monitoring, reproducibility, and versioning
- Experience working with structured and unstructured data and designing preprocessing, enrichment, and transformation pipelines.
- Strong analytical, debugging, and problem-solving skills with the ability to diagnose issues across application, model, pipeline, and infrastructure layers
- Strong written and verbal communication skills and the ability to collaborate effectively with engineering, data science, product, and business stakeholders
- Ability to work effectively with geographically distributed teams across multiple time zones
- Familiarity with Agile/Scrum software development practices.
- Experience working with remote teams spread across multiple time-zones
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 – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.
When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!
Salary transparency
Salary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $123,000 and $180,400. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.
Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging
In-Person Onboarding and Identity Verification
This role may require in-person onboarding and/or in-person ID verification.
