Machine Learning Engineer - Document Intelligence

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.

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.

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 Tensor Flow.

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., Lang Chain/Lang Graph), 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.

Academic Foundation:
Advanced degree (Master’s 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., Deep Eval/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.