Senior Life Sciences Knowledge Engineer (Remote, AI/Data)

Norstella; Dover (DE); Remote; USD 120,000 - 160,000; Full time

Benefits offered by this job

Medical and prescription drug benefits

Health savings accounts or flexible sp

Dental plans and vision benefits

401k retirement plan

Paid parental leave

Paid time off

Job summary

Norstella is seeking a Senior Life Sciences Knowledge Engineer to sit at the intersection of domain science and AI development. This role focuses on curating high-quality fine-tuned datasets and defining model schemas with data scientists and ML engineers.

You will author annotation guidelines, design task taxonomies, and maintain dataset provenance and compliance to enable predictive analytics and insights for clients.

Qualifications

• Graduate degree in life sciences, medical sciences, computer science or equivalent professional experience.
• At least 3 years of professional experience in production-grade life science datasets, including with AI-enabled applications.
• Experience working with structured publishing platforms and data tools; comfort with automation concepts
• Experience working with and statistically analysing large and complex data sets, including data cleaning and preprocessing.
• Experience working with Generative AI, especially LLMs, including agents, throughout the entire software development lifecycle (SDLC).
• Experience creating MCPs and consuming them into Agentic workflows.
• Excellent problem-solving skills and the ability to work independently.
• Excellent communication skills, especially between technical and non-technical teams.

Responsibilities

• Translate complex clinical, regulatory, and life sciences subject matter expertise/requirements into repeatable patterns that can be taught to a model through gold standard examples, working closely with data scientists and machine learning engineers to shape the model’s schema, vocabulary, and target behavior.
• Through close collaboration between SME and technical colleagues, develop novel methods and parameters of model behavior, based on interpretation of requirements and quick iteration cycles.
• Design, build, and continuously refine fine-tuning datasets consisting of input/output pairs that demonstrate desired end-to-end behavior across the target task surface area, edge cases, and known failure modes.
• Author and maintain the annotation and labeling guidelines that govern dataset construction, ensuring the schema, vocabulary, and definition of onboarding outputs remain consistent across contributors.
• Define the task taxonomy and output schema in close partnership with data scientists, ensuring data architecture aligns with downstream evaluation metrics and production requirements across NPD.
• Train and enable subject matter expert graders running eval rounds, including translating feedback to how data scientists implement improvements at the tool call layer.
• Run iterative dataset experiments: identify where the model is failing, design targeted example slices to close those gaps, and partner with the human-in-the-loop SMEs to measure the impact of each dataset change.
• Maintain provenance, licensing, and compliance documentation for every dataset, ensuring all training data meets GxP, regulatory, and intellectual property standards expected in life sciences and clinical settings.
• Conduct new proofs of concept for novel domain capabilities.
• Contribute to Norstella’s knowledge base and taxonomy work and help design new agentic workflows based on domain-grounded language models.

Skills

3+ years life science datasets

Structured data tooling

Data analysis

Generative AI

Agentic workflows

Problem solving

Communication

Education

Graduate degree in life sciences / related field

Job description

Norstella is seeking a Senior Life Sciences Knowledge Engineer to sit at the intersection of domain science and AI development. This role focuses on curating high-quality fine-tuned datasets and defining model schemas with data scientists and ML engineers.

You will author annotation guidelines, design task taxonomies, and maintain dataset provenance and compliance to enable predictive analytics and insights for clients.