Data Scientist/Applied Scientist - Onsite in Seattle

The Role

Unify is seeking an experienced Data Scientist / Applied Scientist to help design and deliver cutting-edge, production-grade AI solutions for our clients. This role is ideal for someone who thrives in complex problem spaces and enjoys translating advanced machine learning techniques into scalable, business-ready applications. This position requires a full-time, 5-day onsite presence in Seattle, enabling close collaboration with client stakeholders and cross-functional delivery teams.

You will work hands-on with Large Language Models (LLMs) and modern NLP techniques, leveraging AWS services such as Amazon Bedrock to build intelligent systems that enhance customer experience and unlock new value. The work spans model development, system design, and active participation in client-facing engagements—ensuring solutions are not only technically robust, but also practical, trusted, and impactful in real-world environments.

Key Responsibilities

• Design, develop, and deploy advanced machine learning and deep learning models to solve complex business problems
• Build and optimize large-scale NLP and Generative AI solutions, including applications leveraging LLMs
• Develop scalable data pipelines and AI architectures to support production-grade deployments
• Implement and support Retrieval-Augmented Generation (RAG) patterns to improve accuracy, relevance, and grounding of model outputs
• Collaborate closely with consulting, engineering, and client teams to support data-driven decision-making
• Contribute to best practices across model development, deployment, and MLOps

Requirements

• Master’s degree in Computer Science, Machine Learning, or a related field
• 5+ years of experience in applied machine learning and deep learning
• Strong proficiency in Python and modern ML frameworks
• Extensive hands-on experience with Large Language Models and transformer-based architectures
• Demonstrated experience deploying ML models into production environments
• Strong experience with AWS, including Amazon Bedrock, SageMaker, Lambda, ECS, and S3
• Familiarity with MLOps practices, tooling, and model lifecycle management
• Hands-on experience designing and implementing RAG architectures

Preferred Experience:

• Knowledge of vector databases and semantic search
• Experience with prompt engineering and model fine-tuning
• Familiarity with containerization and microservices architectures
• Ability to clearly communicate technical concepts to non-technical stakeholders

Please Note