Requirements

• 5+ years of hands-on experience in Machine Learning Engineering or Data Science, or equivalent research experience via a Ph.D.
• Proficiency in Python and modern ML frameworks such as PyTorch or TensorFlow.
• Proven experience building and operating large-scale data processing pipelines using Spark or equivalent distributed frameworks.
• Hands-on experience deploying, scaling, and maintaining ML systems in production on AWS or an equivalent cloud platform.
• Bachelor’s degree in Computer Science, Physics, Mathematics, or a related quantitative field, or equivalent practical experience.

Nice to Haves

• Practical experience or research background in de-identification, group anonymization, Differential Privacy, or synthetic data generation.
• Experience with classification, Named-Entity Recognition (NER), transformer architectures, LLM fine-tuning using the Hugging Face ecosystem, and model inference optimization for GPU hardware.
• Exposure to agent execution, agent orchestration, or LLM evaluation frameworks such as LangGraph or LangSmith.
• Understanding of Responsible AI practices including bias/fairness evaluation and privacy regulatory frameworks such as GDPR and CCPA.
• Ability to translate technical design decisions and privacy-utility trade-offs for legal, compliance, and product partners.

What You'll Be Doing

• Apply and evaluate privacy techniques across enterprise use cases, balancing privacy-utility trade-offs and providing actionable recommendations for data governance.
• Build, fine-tune, and improve de-identification and anonymization models, including Named-Entity Recognition and pattern-matching layers, focusing on accuracy, latency, and compute efficiency.
• Own the full lifecycle of data exploration, transformation, feature and prompt engineering, and model design across high-throughput Spark, EMR, and SageMaker batch pipelines.
• Support data pipelines in production, troubleshooting issues like memory errors and capacity constraints.
• Collaborate with platform, infrastructure, and product engineering teams to translate requirements into scalable ML systems and work with legal and compliance stakeholders on data governance frameworks.
• Communicate de-identification behavior, results, and architectural decisions clearly to technical, legal, and customer-facing audiences.

Perks and Benefits

• Workday Bonus Plan or role-specific commission/bonus eligibility.
• Annual refresh stock grants awarded by Workday Inc.
• Flexible work arrangements combining in-person and remote work, with a minimum of 50% time spent in office or field per quarter.
• Supportive company culture rooted in integrity, empathy, and shared enthusiasm.
• Commitment to reasonable accommodations for qualified individuals during the application process and employment.