Senior Life Sciences Knowledge Engineer

Senior Life Sciences Knowledge Engineer

Company: Norstella

Location: Remote, United States

Employment Type: Full Time

Description

Senior Life Sciences Knowledge Engineer

About us:

Why Norstella? Norstella unites market-leading companies that all have a shared goal of improving patient access. Each organization (Evaluate, Citeline, MMIT, Panalgo, The Dedham Group) delivers must-have answers for critical strategic and commercial decision-making.

Together, we help our clients:

• Assess the market need and competitive landscape

• Know precisely which drugs to prioritize in their portfolios

• Find out where the launch difficulties will be—before they’re difficulties

• Track and improve market access post-launch

By combining the efforts of each organization under Norstella, we can offer an even wider breadth of expertise, cutting-edge data solutions and expert advisory services alongside advanced technologies such as real-world data, machine learning-driven predictive analytics. At Norstella, we don’t just deliver information and insights. We deliver answers you can act on.

Job Description

About the role:

As a Senior Life Sciences Knowledge Engineer at Norstella, you will sit at the intersection of deep scientific domain expertise and applied AI development. This role will be embedded within a group of life science thought leaders, but will interface across cross-functional teams of data scientists, machine learning engineers and data engineers. Your work centers on curating high-quality fine-tuned datasets which speak to the desired end-to-end behavior we want a model to internalize. The datasets and annotation guidelines/frameworks that govern it will play a critical role in our efforts to deliver predictive analytics and insights across clients.

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 “what good output looks like” 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-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.

The guiding principles for success at Norstella:

01: Bold, Passionate, Mission-First

We have a lofty mission to Smooth Access to Life Saving Therapies and we will get there by being bold and passionate about the mission and our clients.Our clients and the mission in what we are trying to accomplish must be in the forefront of our minds in everything we do.

02: Integrity, Truth, Reality

We make promises that we can keep, and goals that push us to new heights.Our integrity offers us the opportunity to learn and improve by being honest about what works and what doesn’t.By being true to the data and producing realistic metrics, we are able to create plans and resources to achieve our goals.

03: Kindness, Empathy, Grace

We will empathize with everyone's situation, provide positive and constructive feedback with kindness, and accept opportunities for improvement with grace and gratitude.We use this principle across the organization to collaborate and build lines of open communication.

04: Resilience, Mettle, Perseverance

We will persevere – even in difficult and challenging situations.Our ability to recover from missteps and failures in a positive way will help us to be successful in our mission.

05: Humility, Gratitude, Learning

We will be true learners by showing humility and gratitude in our work.We recognize that the smartest person in the room is the one who is always listening, learning, and willing to shift their thinking.

Qualifications:

The skills you bring to the table:

• 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.

Bonus points if you have experience in:

• Experience in developing, evaluating, deploying, and monitoring algorithms and models from proof-of-concept, experimental stages through production, in a reproducible, auditable, GxP-compliant manner.

• Experience with the AWS ecosystem, specifically with services like S3, ECS, API Gateway, SageMaker, and Bedrock.

• Familiarity with CI/CD processes, especially as applied to ML operations (MLOps), preferably with Azure DevOps.

• Experience in fast-paced novel development cycles.

Benefits:

• Medical and prescription drug benefits

• Health savings accounts or flexible spending accounts

• Dental plans and vision benefits

• Basic life and AD&D Benefits

• 401k retirement plan

• Short- and Long-Term Disability

• Education benefits

• Paid parental leave

• Paid time off

Norstella is an equal opportunities employer and does not discriminate on the grounds of gender, sexual orientation, marital or civil partner status, pregnancy or maternity, gender reassignment, race, color, nationality, ethnic or national origin, religion or belief, disability or age. Our ethos is to respect and value people’s differences, to help everyone achieve more at work as well as in their personal lives so that they feel proud of the part they play in our success. We believe that all decisions about people at work should be based on the individual’s abilities, skills, performance and behavior and our business requirements. Norstella operates a zero-tolerance policy to any form of discrimination, abuse, or harassment.

Norstella is an equal opportunity employer. All job applicants will receive equal treatment regardless of race, creed, color, religion, alienage or national origin, ancestry, citizenship status, age, physical or mental disability or handicap, medical condition, sex (including pregnancy and pregnancy-related conditions), marital or domestic partner status, military or veteran status, gender, gender identity or expression, sexual orientation, genetic information, reproductive health decision making, or any other protected characteristic as established by federal, state, or local law.