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About This Role
Senior Software Engineer II, AI Developer Foundations
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United States
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#### About Us
At SimplePractice, we are improving access to quality care by equipping health and wellness clinicians with all the tools they need to thrive in private practice.
More than 260k providers trust SimplePractice to build their business through our industry\-leading software with powerful tools that simplify every part of practice management. From admin work to clinical care, our suite of innovative solutions work together to reduce administrative burden, empowering solo and small group practitioners to thrive alongside their clients.
Award\-winning and people\-first, SimplePractice is shaping the future of health tech. Recognized by MedTech Breakthrough, the Digital Health Awards, and BuiltIn's Best Places to Work.
#### Our Culture
At SimplePractice, culture is our foundation. It influences the way we work, how we serve our customers, and how we approach accomplishing our mission. We have five core values that we strive to embody every day:
- We think big
- We take simplicity seriously
- We come as we are
- We act with humility
- We are built on trust
Culture is everyone's responsibility at SimplePractice. Our culture is what drives us to do better for our teammates and customers.
Connection and collaboration are also key to our success. You will work with our talented multi\-national teams and have opportunities to participate in onsites in both the US and Mexico.
#### The Role
Engineers on the AI Developer Foundations will build the tools that build the product. It's a new team that owns how AI gets used inside engineering, product, and design at SimplePractice: the conventions for using these tools, the context agents read before they touch our code, the bar generated work has to clear before a human spends time on it, and the training that makes AI a useful tool to all of engineering.
This is a hands\-on role reporting to the VP of Technology. You'll write code most days, mostly in and around a large Rails monolith, and you'll also write the conventions other engineers follow.
#### Responsibilities
- Own the context layer agents read: rules files, docs conventions, grounding data, with CI and named owners keeping it current
- Publish the standards for how we prompt coding agents, how we supply context, and what a spec needs to contain before it's worth handing to an agent
- Define the review gates any AI\-generated artifact clears before a human spends time on it, and get what reviewers catch flowing back into the conventions
- Take the prototypes running today and turn the ones that hold up into supported workflows that run for someone other than their author
- Evaluate harnesses, models and vendors against criteria and eval sets
- Work the product\-to\-engineering handoff with PMs, designers and EMs
- Teach. Documentation, worked examples, office hours, and time sitting with a squad while they work with the tools we build
- Help us draw the boundaries for where an agent runs unattended, where a human signs off, and where we don't use one at all
#### Desired Skills \& Experience
Education \& Experience
- BS/MS in Engineering, Computer Science, or related field, or equivalent experience
- 7\+ years building production software and then maintaining it. You’ve shipped something, lived with the decisions, and found out whether a design decision held up
- Strong problem\-solving and communication skills; comfortable in fast\-paced, cross\-functional environments
Ruby on Rails
- Recent, direct Rails experience in a large codebase.
- Ability to read unfamiliar application code quickly and judge whether what an agent produced in it is any good
AI\-Assisted Engineering
- LLM\-backed systems you've shipped to users and kept running, past the point where better prompting stops helping
- Experience building context or memory handling, and writing evals
- Judgment about what belongs in code and what belongs in the model
- Track record evaluating tools with evidence
Developer Tooling \& Enablement
- Internal tools, platforms, conventions or workflows that other engineers chose to use.
- CLI and harness\-shaped software, and comfort connecting systems over APIs
- Strong writing. Much of this role is getting other people to work differently, which happens through docs and worked examples rather than announcements
- Comfort working outside engineering, explaining technical constraints to people who don't share your background and taking their pushback seriously
#### Bonus Points
- Agent harnesses, MCP servers, orchestration frameworks
- Healthcare or another HIPAA\-regulated environment
- AWS, Terraform, Kubernetes, Docker
- Experience on a platform or infrastructure team where the customers were other engineers
- Having overbuilt a platform once before the workflows existed, and learning to spot the warning signs earlier
#### Our Stack
- Ruby on Rails monolith, Aurora MySQL, Redis, Sidekiq, Ember.js, AWS (EKS, Bedrock and more). Linear, GitHub, Semaphore, Datadog, LaunchDarkly.
#### Benefits
We offer a competitive benefits program including:
- Medical, dental, vision, life \& disability insurance
- 401(k) plan with company match
- Flexible Time Off (FTO), wellbeing days, paid holidays, and summer Fridays
- Mental health resources
- Paid parental leave \& Backup Care
- Tuition reimbursement
- Employee Resource Groups (ERGs)
#### California Job Applicant Privacy Notice
Thank you for your interest in opportunities at SimplePractice LLC (“SimplePractice” or “us” or “we” or “our”). Please note that when you submit your resume or application materials to us for employment purposes, you are subject to the SimplePractice California Job Applicant Privacy Notice.
For more information about our privacy practices, please contact us at [email protected].
#### Notice to Candidates
SimplePractice has been made aware of fraudulent job postings and unaffiliated third parties posing as our recruiting team. We do not have any affiliation or connection to these situations and only post open roles on our official Careers page (simplepractice.com/careers) and reputable job boards like our official LinkedIn or Indeed pages.
All official SimplePractice recruitment emails will be sent from the domains @simplepractice.com, no\[email protected] or no‑[email protected] email addresses.
Examples of fraudulent domains include careers\-simplepractice.com, simplepractices.com, and simplepractice.careers.
Please note that SimplePractice will never ask candidates or new hires for money or payment of any kind at any stage of the recruitment or onboarding proces
Base salary is one component of total compensation. Employees may also be eligible for an annual bonus or commission. Some roles may also be eligible for overtime pay.
The amount below represents the expected annual base compensation range for this job requisition. Ultimately, in determining your pay, we’ll consider many factors including, but not limited to, skills, experience, qualifications, geographic location, and other job\-related factors.
Base Compensation Range
$208,000—$260,000 USD
Salary Context
This $208K-$260K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $185K across 231 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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At SimplePractice, 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 $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($234K) sits 7% above the category median. Disclosed range: $208K to $260K.
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.
SimplePractice AI Hiring
SimplePractice has 1 open AI role right now. They're hiring across AI Software Engineer. Based in US. Compensation range: $260K - $260K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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