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About This Role
Software Engineer (AI Quality & Reliability)
Location: New York City | In-office
The Mission
“Leading healthcare into the agentic era.”
Every day, patients call their clinic to schedule an appointment, ask a question, or request a prescription refill. Behind each conversation is a busy healthcare team coordinating people, information, and systems.
Taxo builds Voice AI agents that take on this administrative work. Our agents connect with EHR and practice management systems to help teams handle patient intake, complex scheduling, and refill requests, with reliable workflows and clear handoffs to people.
The Opportunity: Make AI Quality Measurable
As our Software Engineer (AI Quality & Reliability), you’ll build the automated evaluation and regression systems that show whether Taxo is ready to release. You’ll turn product acceptance criteria and real failure modes into repeatable tests across voice agents, application workflows, and integrations. This is a software engineering role focused on test infrastructure, independent quality evidence, and reliable releases.
What You’ll Do
- Build the Evaluation System: Own shared evaluation frameworks, test harnesses, and versioned datasets using synthetic or appropriately authorized, privacy-safe data. Make runs repeatable and results easy to investigate.
- Test the Whole Workflow: Build automated end-to-end regression coverage across conversations, tool calls, application behavior, and integration boundaries, including failures, recovery, and human escalation.
- Measure AI Behavior: Evaluate task completion, tool-use correctness, latency, and conversation quality. Combine deterministic checks with calibrated model-assisted scoring and appropriate human review.
- Make Releases Reviewable: Produce release-readiness evidence, maintain quality dashboards, and coordinate quality gates with product and engineering. Surface regressions, coverage gaps, and unresolved risks before launch.
- Turn Failures into Tests: Reproduce defects, isolate failure patterns, and add durable regression cases. Engineering service owners retain component tests, production instrumentation, on-call response, and fixes; you independently verify the results.
- Improve the Test System: Integrate automated checks into CI/CD, reduce flaky tests, and keep test environments and fixtures useful as the product evolves. Partner with domain reviewers on healthcare-specific cases and escalate questions that require clinical judgment.
Who You Are
- The Test Engineer: Strong software engineering skills in Python or TypeScript and experience building maintainable test automation, reusable fixtures, and CI pipelines.
- The AI Evaluator: Practical experience evaluating LLM-powered applications, agents, or voice systems, including nondeterministic behavior and the limits of automated scoring.
- The Systems Thinker: Experience testing APIs, asynchronous workflows, and distributed systems, with the ability to trace a failure across service boundaries.
- The Investigator: A habit of turning ambiguous failures into reproducible cases, actionable evidence, and reliable regression coverage.
- The Quality Partner: Clear communication about risk and release readiness, with sound judgment around sensitive data and human review.
- Bonus: Experience with streaming audio, telephony, healthcare workflows, EHR integrations, or reliability testing in privacy-sensitive products.
Why Taxo
Healthcare teams should be able to spend more time on patients and less time on administration. Help us build the products and company that make that possible.
- Practical AI Work: Build evaluation systems for real-world AI behavior across healthcare workflows.
- Impact That Matters: Help catch failures before they disrupt patients or clinic teams.
- Technical Ownership: Shape shared evaluation and test infrastructure that improves every release.
Compensation
Base salary: $130,000–$170,000 USD annually
Equity: 0.10%–0.25%
Benefits: Health coverage
How We Work
Taxo is a small, fast, high-conviction team. We care deeply about the problem, move with urgency, and take ownership of the work. We collaborate closely, make practical decisions, and keep improving through what we learn from customers.
If you want to build the engineering systems that make healthcare AI more dependable, we’d love to hear from you.
Salary Context
This $130K-$170K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $192K across 198 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 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At TAXO, 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 $220,000 based on 1,601 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 32% below the category median. Disclosed range: $130K to $170K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
TAXO AI Hiring
TAXO has 2 open AI roles right now. They're hiring across AI Software Engineer. Based in New York, NY, US. Compensation range: $170K - $220K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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