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About This Role
About Signant Health
At Signant Health, we help bring life\-changing treatments to patients faster. We are a global evidence generation company that supports clinical trials with smart technology, scientific expertise, and hands\-on operational support — so better data leads to better decisions in healthcare. We embrace AI and advanced technologies to enhance every aspect of what we do, from data analysis to operational efficiency.
Our teams work at the intersection of science, technology, and patient experience, delivering digital solutions powered by AI innovation that make clinical trials more efficient, more accurate, and more accessible around the world. Trusted by leading pharmaceutical companies and CROs, our platforms and services support studies across more than 90 countries and have contributed to hundreds of new drug approvals.
If you are motivated by meaningful work, global impact, and innovation in clinical research and digital health — including the opportunity to work with cutting\-edge AI technologies — you will find purpose and opportunity at Signant Health.
About the Role
Signant Health is looking for a Software Engineer – AI Accelerated Development to build AI\-powered, agentic features into our clinical trial platforms. Working across the .NET stack, you will take AI capabilities from backend LLM integration through to a usable front\-end — designing agents and agentic workflows, not just prompting a model. This is a hands\-on engineering role for someone who works with agentic tooling every day and wants to ship production AI in a regulated, high\-impact environment. We weigh the depth of your AI and agentic build experience more heavily than years of general experience: a developer with one to three years of substantive, hands\-on agentic work is exactly the profile we are targeting.
KEY ACCOUNTABILITIES – Function
- Design and build AI agents and agentic workflows — tool\-use/function calling, multi\-step task orchestration, and agent loops — that power Signant Health's clinical trial platforms.
- Integrate LLM APIs (Anthropic, OpenAI, Bedrock, or Azure OpenAI) into .NET application code, handling authentication, streaming, error handling, and cost/latency tradeoffs.
- Deliver AI features end\-to\-end, taking them from backend integration through to a usable, production\-ready front\-end.
- Build custom agents and tools with the Claude Agent SDK, wiring and steering agent loops.
- Author custom skills and slash commands for Claude Code to codify team\-specific workflows.
- Implement RAG pipelines and integrate vector stores (e.g. Qdrant, pgvector, Pinecone) to ground agent outputs in trusted data.
- Instrument agents for evaluation and observability so their outputs can be verified, measured, and audited.
- Apply AI\-specific risk controls appropriate to a regulated clinical environment, addressing hallucination, determinism, and auditability of agent decisions.
- Use Claude Code (or an equivalent agentic CLI) daily as a primary development tool, reviewing and steering AI\-generated diffs.
KNOWLEDGE, SKILLS \& ATTRIBUTES
Essential:
- Full\-stack .NET development — C\#/ASP.NET Core on the backend paired with a modern front\-end framework.
- Hands\-on agentic development experience — building AI agents or agentic workflows (tool\-use/function calling, multi\-step task orchestration, agent loops); not just prompting an LLM, but building systems around one.
- Practical LLM API integration (Anthropic, OpenAI, Bedrock, or Azure OpenAI), including authentication, streaming, error handling, and cost/latency tradeoffs.
- Proven full\-stack delivery of AI features — able to take an agentic/AI feature from backend integration through to a usable front\-end, not just a notebook prototype.
- Daily, hands\-on use of Claude Code (or an equivalent agentic CLI) as a primary development tool — comfortable working through multi\-step agentic sessions and reviewing and steering AI\-generated diffs, rather than occasional autocomplete\-style use.
- Prompt engineering — designing and iterating on system prompts as production software contracts, not one\-off experiments.
- Python proficiency as the preferred language for AI/agent tooling; comfort with async patterns a plus.
- Evaluation and observability literacy — able to reason about whether an agent's output is correct and instrument it to prove so.
Desirable:
- Claude Agent SDK experience — building custom agents, defining tools, and wiring agent loops.
- Working knowledge of agent primitives: hooks (deterministic pre/post\-tool\-call controls), skills (progressive\-disclosure, load\-on\-demand instructions), subagents/multi\-agent delegation, and session/state management.
- Experience authoring custom skills or slash commands for Claude Code to codify team\-specific workflows.
- Exposure to MCP (Model Context Protocol) for connecting agents to internal tools and data sources.
- Working knowledge of RAG patterns and vector store integration (e.g. Qdrant, pgvector, Pinecone).
- Awareness of AI\-specific risk areas relevant to a regulated environment (OWASP LLM Top 10, hallucination/determinism concerns, and auditability of agent decisions).
Why Signant Health?
At Signant Health, your work has real impact. Everything we build, support, and deliver helps advance clinical research and bring new treatments to patients faster — improving lives around the world. Our teams combine science, technology, and operational expertise to solve complex clinical trial challenges, and every role contributes to that mission.
We offer a collaborative, global environment where you can grow your career while working alongside experts across clinical, technology, data, and operations, with opportunities to learn, take ownership, and drive meaningful innovation — not just maintain the status quo.
If you are looking for purpose\-driven work, smart colleagues, and the opportunity to help shape the future of clinical research and digital health, Signant Health is the place to do it.
At Signant Health, accepting difference isn't enough — we celebrate it, we support it, and we nurture it for the benefit of our team members, our clients, and our community. We are proud to be an equal opportunity workplace and an affirmative action employer, committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or veteran status. Prior to their start date, all candidates are required to be verified through a thorough background check and identity verification to confirm eligibility for employment.
\#LI\-JM1
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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Signant Health, 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 $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Signant Health AI Hiring
Signant Health has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Blue Bell, PA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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