Interested in this AI Software Engineer role at United Biosource Corporation?
Apply Now →About This Role
*As a pharmaceutical support industry leader,* *UBC* *is devoted to* *empowering health solutions for a better tomorrow. We take* *pride* *in* *improving patient outcomes* *and* *advancing healthcare****. At UBC, we provide services to enhance the entire drug development process and commercialization lifecycle \- from clinical trial support to real\-world evidence generation.*
*Embark on a* *rewarding career journey with UBC!**Grow* *your career while making a meaningful* *impact* *on the* *world* *around you.* *UBC* *fosters a culture built on our Core Values of Respect, Accountability, Innovation, Quality, Integrity, and Collaboration. We believe in an* *inclusive**workplace* *that fosters creativity.*
*If you are seeking a career that will* *challenge,* *inspire**, and* *reward* *you, join us at* *UBC****!*
Job Title: ISE01I \- Sr Software Development Engineer
Brief Description:
As a Principal Software Development Engineer in the United States, you will bring advanced technical depth and leadership to architect, design, and develop sophisticated software systems. This role is hands\-on, with a requirement for up to 100% coding, ensuring quality and maintainability across projects. You will provide tactical day\-to\-day technical guidance to less experienced team members, acting as a mentor to support their growth. You’ll also partner with other technology leaders to shape the overall technology direction, with a focus on pattern management and system consistency.
This role demands cross\-functional collaboration and interaction with stakeholders to ensure technology delivery aligns with organizational goals. You will lead troubleshooting efforts and conduct root cause analyses on relevant issues to maintain high system reliability. As a recognized technical authority, you’ll represent the organization in cross\-functional and vendor partner settings.
Specific job duties:
- Drive collaboration with multiple vendors to establish comprehensive estimates, delivery plans, and timelines for projects or programs within an application family.
- Ensure the technological integrity and quality of vendor solutions, providing accountability across concurrent projects or programs.
- Lead the delivery and technical guidance across an application family, ensuring alignment with strategic initiatives
- Serve as a technical leader for large or complex projects, including:
+ Strategizing to optimize and modernize technology, focusing on technical debt reduction where applicable.
+ Identifying and implementing process improvements to enhance team efficiency and technology support.
+ Leading knowledge\-sharing efforts to strengthen internal and vendor partner expertise.
+ Acting as a subject matter expert on intricate components of the application environment.
- Partner with architects to influence strategic decisions on the use, retirement, or addition of technology within the enterprise architecture.
- Provide high\-level technical expertise to projects involving multiple complex technology components.
- Establish and promote the best practices, reusable components, and guidelines to optimize technology usage.
- Lead incident response and problem resolution for critical production issues, ensuring thorough root cause analysis and long\-term solutions.
- Take on special projects or initiatives as assigned, often with organization\-wide visibility and impact.
Desired Skills and Qualifications:
- Bachelor’s degree in computer science, Engineering, or a related field (Master’s degree preferred) or equivalent work experience.
- 12–15 years of relevant experience, including at least 3–5 years at a senior level or in a similar principal engineering role.
- An innovation mindset.
- Extensive coding experience including .NET Unified Platform V8 and .NET Framework 4\.8
- Experience with implementing workflow orchestration engines into microservice based software architectures. Preference for experience with Camunda Cloud.
- Thorough knowledge and application of advanced technical principles, theories, and concepts in software engineering.
- Ability to design and implement innovative solutions that align with complex organizational goals and objectives.
- Strong customer orientation with an ability to convey technical concepts to stakeholders at all organizational levels.
- In\-depth knowledge of the healthcare or PBM industry is preferred.
- Extensive experience working within a vendor partner model, ensuring collaboration and quality.
- Flexibility to work outside standard hours to meet critical project deadlines.
- Demonstrated ability to manage multiple priorities, adapt to dynamic work environments, and meet project deadlines.
- Strong collaborative skills, with experience working across multidisciplinary project teams.
- Extensive experience with Agile development.
- Experience with Java or react development a plus.
Benefits:
At UBC, employee growth and well\-being are always at the forefront. We offer an extensive range of benefits to ensure that you have everything you need to thrive personally and professionally. Here are some of the exciting perks UBC offers:
- Competitive salaries
- Growth opportunities for promotion
- 401K with company match\*
- Tuition reimbursement
- Flexible work environment
- Discretionary PTO (Paid Time Off)
- Paid Holidays
- Employee assistance programs
- Medical, Dental, and vision coverage
- HSA/FSA
- Telemedicine (Virtual doctor appointments)
- Wellness program
- Adoption assistance
- Short term disability
- Long term disability
- Life insurance
- Discount programs
*UBC is proud to be an equal opportunity employer and does not discriminate because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state, or local protected class. We are committed to a diverse, equitable, and inclusive culture that fosters respect for each other, our clients, and our patients.*
\#LI\-JS1
\#LI\-REMOTE
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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At United Biosource Corporation, 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 in Demand for This Role
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 $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
United Biosource Corporation AI Hiring
United Biosource Corporation has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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