Description:

BenchSci is seeking a Senior Machine Learning Engineer to join their Knowledge Enrichment team.
The role involves designing and implementing ML-based approaches to analyze, extract, and generate knowledge from complex biomedical data.
You will work with both publicly available and proprietary internal data, represented in unstructured text and knowledge graphs.
The position focuses on enriching BenchSci’s knowledge graph through various ML techniques, including classification and discovering high-value implicit relationships.
You will collaborate with team members to apply state-of-the-art ML and graph ML/data science algorithms.
The role requires working in a team that challenges the status quo and focuses on delivering value in a fail-fast environment.
Responsibilities include analyzing a large biological knowledge graph, developing knowledge enrichment strategies, delivering robust ML models, and collaborating with cross-functional teams.
You will also provide technical leadership on projects and ensure the adoption of ML best practices within the team.

Requirements:

A minimum of 3 years of experience as an ML engineer, ideally 5+ years.
Experience in providing technical leadership on complex projects is preferred.
A degree in Software Engineering, Computer Science, or a related field is required, with a PhD being preferable.
Proven track record of delivering complex ML projects in agile environments.
Demonstrable proficiency in ML, particularly with NLP and ML techniques.
Mastery of several ML frameworks and libraries, with the ability to architect complex ML systems.
Extensive experience with Python and PyTorch is essential.
Experience with the full ML development lifecycle, from architecture to deployment and maintenance.
Familiarity with Large Language Models and Retrieval Augmented Generation (RAG) architecture is required.
Experience with graph machine learning and knowledge graphs, ideally in a biological context.
Strong problem-solving skills and attention to detail regarding scalability and performance.
Comprehensive knowledge of software engineering principles and programming fundamentals.
Experience with data manipulation tools such as SQL, Cypher, or Pandas is necessary.
A proactive attitude and ability to work in cross-functional teams are essential.
Outstanding verbal and written communication skills are required to explain complex concepts to various stakeholders.
A growth mindset and engagement with the ML/AI community are important.

BenchSci offers a culture that prioritizes team members and fosters transparency, collaboration, and continuous learning.
The company invests in its people, providing resources for personal growth and development.
Employees are empowered to unleash their full potential and thrive in a challenging environment.
BenchSci is committed to diversity, equity, and inclusion, creating an inclusive environment for all backgrounds.
Accessibility accommodations are available for those who require them.