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About This Role
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:
$132,400\.00 \- $217,600\.00
Bio\-Techne develops innovative software and instrumentation solutions that help scientists generate accurate, reproducible biological data at scale. As 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.
Responsibilities:
- 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
Qualifications
Education \& Experience:
- 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
Knowledge, Skills, and Abilities:
- 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 track record of designing and building novel systems or components, rather than primarily integrating off\-the\-shelf tools
- Strong computer\-science fundamentals: data structures, algorithmic complexity
Preferred Qualifications:
- Experience with cloud platforms such as AWS or Azure
- Familiarity with Kubernetes and multi\-service deployment strategies
- Experience managing the ML lifecycle, including but not limited to EDA, feature engineering, model training/tuning, validation, deployment, and maintenance
- Experience with vector databases (pgvector, FAISS, Pinecone, or similar)
- Experience with RAG frameworks such as LangChain or LlamaIndex
- Exposure to scientific software, laboratory instrumentation, or regulated environments (GxP)
- Experience designing systems with multi\-tenant SaaS considerations and feature\-based licensing
Why Join Bio\-Techne:
- ### We offer competitive insurance benefits starting on day one: medical, dental, vision, life, short\-term disability, long\-term disability, pet, and legal and ID shield.
- ### We invest in our employees’ financial futures through 401k plans, an employee stock purchase plan (ESPP), Health Saving Account (HSA), Flexible Spending Account (FSA), and Dependent Care FSA.
- ### We empower our employees develop their careers through mentorship, promotional opportunities, training and development, tuition reimbursement, internship programs, and more.
- ### We offer employee resource groups, volunteer paid time off, employee events, and charity drives to build a culture of caring and belonging.
- ### We offer an accrued leave policy with paid holidays, paid time off, and paid parental leave.
- ### We foster a culture of empowerment and innovation, where employees feel valued and encouraged to bring their new ideas to the table.
Bio\-Techne is an E\-Verify Employer in the United States.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
To protect the interests of all, Bio\-Techne will not accept unsolicited resumes from any source other than a candidate application. Any unsolicited resumes sent to Bio\-Techne will be considered Bio\-Techne property.
*If you require a reasonable accommodation to complete an application, participate in an interview, or take part in any other stage of the recruitment process, please contact* *hr@bio\-techne.com* *for assistance.*
Salary Context
This $132K-$217K range is below the median for AI Software Engineer roles in our dataset (median: $190K across 219 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Bio-Techne, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($175K) sits 25% below the category median. Disclosed range: $132K to $217K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Bio-Techne AI Hiring
Bio-Techne has 1 open AI role right now. They're hiring across AI Software Engineer. Based in San Jose, CA, US. Compensation range: $217K - $217K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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