Software Engineer - AI Engineer II/III

$80K - $140K Rancho Santa Margarita, CA, US Mid Level AI Software Engineer

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Skills & Technologies

AnthropicAutogenAwsAzureCrewaiDockerFaissHugging FaceKubernetesLangchain

About This Role

AI job market dashboard showing open roles by category

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Applied Medical is a new generation medical device company with a proven business model and commitment to innovation fueled by rapid business growth and expansion. Our company has been developing and manufacturing advanced surgical technologies for over 35 years and has earned a strong reputation for excellence in the healthcare field. Our unique business model, combined with our dedication to delivering the highest quality products, enables team members to contribute in a larger capacity than is possible in typical positions.

Position Description:

We are seeking a highly skilled AI Engineer with deep expertise in Large Language Models (LLMs), AI agent frameworks, and Model Context Protocols (MCP). The ideal candidate will design, develop, and deploy advanced AI\-powered applications that leverage LLMs for reasoning, planning, and autonomous decision\-making. In this role, you will build agentic systems capable of performing multi\-step tasks, interacting with external systems, integrating with APIs and tools, and orchestrating complex workflows end\-to\-end. You will also implement and optimize MCP\-based architectures to ensure reliable tool usage, persistent context management, and robust coordination between agents and backend services.

Collaboration is a fundamental part of our organization's culture and is essential to our continued success. As such, the successful candidate for this position is expected to work on\-site, enabling them to engage fully with colleagues and contribute to cross\-functional initiatives. Therefore, the ability to work collaboratively and contribute to a positive and supportive team environment is a key requirement for this role.

Key Responsibilities:

  • LLM Application Development: Design, fine\-tune, and deploy LLM\-based applications for reasoning, planning, retrieval, and automation.
  • Agent Systems: Build autonomous AI agents that interact with APIs, databases, and tools using frameworks like LangChain, LangGraph, AutoGen, or custom solutions.
  • Context \& Memory Management: Implement Model Context Protocols (MCP) for persistent context, robust coordination between agents, and seamless tool usage.
  • Data \& RAG Pipelines: Develop RAG pipelines, vector databases, and prompt\-engineering strategies to enable context\-aware responses.
  • Backend \& Infrastructure: Build scalable backend systems and deploy solutions on cloud or on\-prem environments (Azure, AWS), with containerization (Docker, Kubernetes).
  • Research \& Innovation: Stay up\-to\-date with emerging LLM and agentic technologies and evaluate them for practical applications.
  • Collaboration \& Governance: Work closely with data science, product, and engineering teams while ensuring responsible AI practices, model safety, and compliance with data privacy regulations.

*

Position Requirements:

This position requires the following skills and attributes:

  • Bachelor’s or Master’s in Computer Science, AI/ML, or related field (Ph.D. preferred).
  • 2\+ years building and deploying AI applications with LLMs (OpenAI, Anthropic, HuggingFace).
  • Strong Python skills, with experience in Git, FastAPI/Flask, and ML frameworks (PyTorch, TensorFlow).
  • Hands\-on experience with agent frameworks (AutoGen, LangGraph, CrewAI) and orchestration strategies.
  • Proficiency in vector databases (Pinecone, Milvus, ChromaDB, FAISS) and embedding techniques.
  • Solid understanding of prompt engineering, RAG, and LLM optimization.
  • Familiarity with cloud/on\-prem deployment and containerization (Docker, Kubernetes).
  • Experience implementing Model Context Protocols for context management and interoperability.
  • Experience with multi\-agent systems, planning algorithms, or reinforcement learning with LLMs.
  • Knowledge of production\-grade AI monitoring, logging, and observability.
  • Excellent problem\-solving skills and ability to thrive in fast\-paced, collaborative environments.

Benefits:

  • Competitive compensation range: $80000 \- $140000 / year (California).
  • Comprehensive benefits package.
  • Training and mentorship opportunities.
  • On\-campus wellness activities.
  • Education reimbursement program.
  • 401(k) program with discretionary employer match.
  • Generous vacation accrual and paid holiday schedule.

*Please note that the compensation range may be adjusted in the future, and bonus and incentive compensation plans may apply.*

Our total reward package reflects our commitment to employee growth and well\-being, as we invest in your development and offer a range of benefits designed to enhance your career and life. *All compensation and benefits are subject to plan documents and written agreements.*

Equal Opportunity Employer

Applied Medical is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, disability (mental and physical), exercising the right to family care and medical leave, gender, gender expression, gender identity, genetic information, marital status, medical condition, military or veteran status, national origin, political affiliation, race, religious creed, sex (including pregnancy, childbirth, breastfeeding and related medical conditions), or sexual orientation, or any other status protected by federal, state or local laws in the locations where Applied Medical operates.

Salary Context

This $80K-$140K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $190K across 193 roles with salary data).

Role Details

Company Applied Medical
Title Software Engineer - AI Engineer II/III
Location Rancho Santa Margarita, CA, US
Category AI Software Engineer
Experience Mid Level
Salary $80K - $140K
Remote No

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 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Applied Medical, 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

Anthropic (6% of roles) Autogen (3% of roles) Aws (31% of roles) Azure (23% of roles) Crewai (3% of roles) Docker (10% of roles) Faiss (1% of roles) Hugging Face (4% of roles) Kubernetes (12% of roles) Langchain (11% of roles)

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 $234,620 based on 682 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($110K) sits 53% below the category median. Disclosed range: $80K to $140K.

Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.

Applied Medical AI Hiring

Applied Medical has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Rancho Santa Margarita, CA, US. Compensation range: $140K - $140K.

Location Context

Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.

The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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

Based on 682 roles with disclosed compensation, the median salary for AI Software Engineer positions is $234,620. Actual compensation varies by seniority, location, and company stage.
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.
About 16% of the 3,824 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Applied Medical is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Software Engineer positions include Staff Engineer, AI Architect, Engineering Manager. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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