Machine Learning Engineer Document Intelligence

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About the Team

About the Role

We are looking for a Machine Learning Engineer to join the ML - Document Intelligence team to drive the design and development of our core Document Intelligence Platform as a Service. In this role you will work on building and optimizing critical features like generic document entity extraction entity resolution and document classification leveraging cutting-edge AI/ML techniques.

Your primary focus will be to:

• Support the design and implementation of LLM-based technologies for document parsing entity extraction and classification tasks.

• Apply traditional ML and deep learning techniques to continuously enhance the accuracy efficiency and scalability of our document intelligence models.

• Build scalable ML pipelines and services for data preprocessing feature engineering training and inference enabling high-volume document processing workflows.

• Perform exploratory data analysis (EDA) on diverse document datasets to uncover valuable insights optimize feature engineering and inform model development.

You will also:

• Collaborate with software engineers Workday app developers product managers and other ML teams

• Take ownership for finding creative solutions that move projects forward

• Write clean maintainable and testable code following best practices in software engineering including automation observability and scalability.

• Conduct code reviews participate in design discussions and engage in collaborative team activities like hackathons and knowledge-sharing sessions.

About You

Basic Qualifications:

• Deep Technical ML Capability: 3 years of experience researching developing and deploying production-grade ML systems including expertise in deep learning NLP Information Retrieval and recommender systems using frameworks like PyTorch or TensorFlow.

• Generative AI & Agentic Systems: Proven track record of building and evaluating NLP and LLM-powered products including expertise in RAG architectures agentic frameworks (e.g. LangChain/LangGraph) and long-context LLM applications (e.g. Text-to-SQL).

• Engineering Excellence: 2 years of Python experience with a focus on modular library design asynchronous patterns and scalable system architecture (state management/error handling) for non-deterministic AI outputs.

Other Qualifications

• Academic Foundation: Advanced degree (Masters or Ph.D.) in a quantitative field or a strong portfolio of peer-reviewed research publications.

• Optimization & Advanced Techniques: Proficiency in techniques like DSPy Reinforcement Learning imitation learning graph neural networks multi-modal models and large-scale data processing (PySpark SQL).

• Experimental Rigor: A test-everything mindset with experience in A/B testing Knowledge Graphs and Golden Dataset curation for model benchmarking.

• Data Pipelines: Proficiency in large-scale data processing (PySpark SQL).

• Production MLOps: Hands-on experience with the full ML lifecycle including model fine-tuning (PEFT) evaluation frameworks (e.g. DeepEval/RAGAS) and cloud-native deployment (Docker/K8s AWS/GCP).

• Collaborative Leadership: Demonstrated ability to lead cross-functional teams mentor junior engineers and solve ambiguous problems with high autonomy.

Workday Pay Transparency Statement

The annualized base salary ranges for the primary location and any additional locations are listed below. Workday pay ranges vary based on work location. As a part of the total compensation package this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidates compensation offer will be based on multiple factors including but not limited to geography experience skills job duties and business need among other things. For more information regarding Workdayscomprehensive benefits in Canada please click here. For more information regarding Workdays comprehensive benefits in the US please click here.

Our Approach to Flexible Work

With Flex Work were combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections maintain a strong community and do their best work. We know that flexibility can take shape in many ways so rather than a number of required days in-office each week we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers prospects and partners (depending on role). This means youll have the freedom to create a flexible schedule that caters to your business team and personal needs while being intentional to make the most of time spent together. Those in our remote home office roles also have the opportunity to come together in our offices for important moments that matter.

Pursuant to applicable Fair Chance law Workday will consider for employment qualified applicants with arrest and conviction records.

Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans.

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