Senior Software Engineer, AI and ML Platforms

By joining Bio-Techne, you’ll join a company with a powerful and positive purpose of enabling cutting-edge research in Life Sciences and Clinical Diagnostics. Bio-Techne, and all of its brands, provides tools for researchers to further treat and prevent disease worldwide.

Pay Range:

$ - $

Bio-Techne develops innovative software and instrumentation solutions that help scientists generate accurate, reproducible biological data  our software portfolio evolves toward SaaS-based delivery models, we are embedding AI-driven intelligence directly into our platforms to improve usability, automation, and scientific insight.

This Senior Software Engineer role sits at the intersection of AI engineering, cloud-native microservices, and enterprise SaaS platforms. You will lead the design and implementation of scalable backend services that power AI-enabled features across Bio-Techne software products. This role is ideal for an experienced engineer who enjoys owning architecture, mentoring others, and delivering production-grade systems used in regulated scientific environments.

This is a hybrid position based out of our San Jose, CA site.

• Lead the design and development of cloud-native, microservices-based backend systems supporting Bio-Techne software products

• Design, build, and deploy AI-powered services, including LLM-based assistants, recommendations, and automation workflows

• Develop scalable REST and event-driven APIs that integrate AI services with instrument software and customer-facing applications

• Architect and implement Retrieval-Augmented Generation (RAG) pipelines over scientific, operational, and customer data

• Partner with central IT, Enterprise Data, and Infrastructure teams to align AI services with shared platform standards. This includes MLOps practices, data access governance, observability frameworks, and security controls, ensuring that POC work can be reliably promoted to production environments.

• Establish and maintain MLOps practices for model versioning, evaluation, monitoring, and retraining — ensuring AI services degrade gracefully and remain reliable over time.

• Collaborate with product management, scientists, and UX teams to translate scientific workflows into AI-driven software capabilities

• Ensure reliability, observability, security, and performance of distributed services operating in production environments

• Drive technical standards for code quality, service ownership, and system architecture

• Mentor junior engineers and contribute to design reviews, code reviews, and technical decision-making

• Document system architecture, APIs, and operational considerations for internal and cross-functional stakeholders

• B.S. in Computer Science, Software Engineering, or related technical field and 7+ years of relevant experience developing and operating production-grade software systems

• Or, M.S. in Computer Science, AI/ML, or related discipline and 5+ years of relevant experience

• Or, equivalent combination of relevant education and experience

• Strong proficiency in Python, Java, or similar backend languages with hands‑on microservices experience

• Demonstrated experience designing and operating cloud-native SaaS platforms

• Experience building RESTful APIs using frameworks such as FastAPI, Flask, or Spring Boot

• Hands‑on experience integrating AI/ML or LLM-based services into real-world applications

• Solid understanding of distributed systems, asynchronous processing, and service‑to‑service communication

• Experience with containerization (Docker) and CI/CD pipelines

• Strong written and verbal communication skills, including experience working across engineering and scientific teams

• Strong ability to understand how systems work under the hood, with the ability to reason about and implement the underlying algorithms, evaluate the tradeoffs, and build new capabilities

• Demonstrated ability to implement ML or information‑retrieval algorithms; not solely through high-level frameworks (examples: custom retrieval, ranking and re‑ranking strategies, embedding and chunking approaches, evaluation pipelines, or inference‑time optimizations)

• Demonstrated…