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About This Role
Description:
At Post Acute Analytics, we have developed a state-of-the-art solution for resolving healthcare's biggest
challenges, and we are on path to become the number one solution for supporting patient journeys,
creating utilization management efficiencies, and helping providers and payors deliver the highest quality
of care.
We are seeking a Senior Software Engineer to join our Data Science team and take ownership of one of
the clinical AI models at the core of our platform. Our team builds production systems that use large
language models (LLMs) to read clinical documentation and support clinical decision making. You will own
one of those systems end to end — its accuracy, its releases, and its behavior in production. This role sits
at the intersection of production software engineering and applied AI with high standards for both.
What You Will Do
- Own a production clinical AI model end to end: iteration, releases, accuracy, and production incident response.
- Collaborate with other engineers and clinical team members to design and maintain the evaluation systems that prove model correctness.
- Partner directly with our clinical team on model audits and requirements, translating clinical rules into system behavior and explaining model output to clinical and product stakeholders.
- Evaluate and integrate new foundation models, including open-weight alternatives, balancing accuracy against latency and cost.
- Act as the internal escalation point when a client or clinician reports issues or has questions about a model.
- Maintain the runtime inference infrastructure in partnership with other engineers.
- Collaborate within an Agile team, engaging in sprint planning, daily standups, backlog refinement, code review, and design discussions. Partner with Product, DevOps, InfoSec, and other engineering teams to align development efforts with business goals.
Technical Experience
- 6 or more years of professional software engineering experience.
- Strong Python skills building and maintaining production services and pipelines.
- Demonstrated experience designing evaluations for AI systems.
- Practical depth in prompt and context engineering.
- Experience with Docker and Kubernetes.
- Experience with PostgreSQL or similar relational databases.
- Experience with event-driven architecture patterns (Kafka or similar).
Requirements: Nice To Have
- Experience with agentic AI patterns and coding tools.
- Experience with AWS and AWS Bedrock.
- Node.js, TypeScript, React experience.
- LLM fine-tuning experience.
- Advanced statistics, NLP, feature engineering, and/or classical machine learning.
Professional Skills
- Strong collaboration skills with a team-oriented mindset.
- Demonstrated interest / experience in the health care industry.
- Excellent communication, including the ability to explain model behavior and its limitations to non-technical clinical and product stakeholders.
- Comfort with ambiguity.
- Ownership mindset with a track record of being accountable for a system in production rather than delivering tickets.
- Passion for best practices and continuous improvement in software engineering and applied AI.
What We Provide
- Competitive compensation commensurate with experience.
- 401K.
- Health, dental, and disability insurance.
- Flexible, results-oriented work schedule and time off.
- Platform for rapid career growth.
PAA is based out of Dallas, TX and this role will be performed remotely. This role will require 0-5% travel
for internal meetings or client interactions.
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 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Post Acute Analytics, 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 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Post Acute Analytics AI Hiring
Post Acute Analytics has 1 open AI role right now. They're hiring across AI Software Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
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