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About This Role
The Impact You'll Make
You’ll build new applications and modernize existing products using AI-enabled software development, helping Turbocor advance the software behind oil-free compressors used in data centers, commercial buildings, and district cooling systems worldwide.
You’ll establish practical, repeatable ways to use AI across the development lifecycle, from agentic workflows and rapid prototyping to testing, code review, and production deployment. You’ll work alongside software, mechanical, electrical, service, and project engineers to turn strong ideas into reliable, maintainable solutions.
Success in this role means shipping valuable software faster, improving developer productivity, and helping the broader team use AI tools effectively while accounting for their limitations.
What You’ll Be Doing
Design, build, and ship applications across the product portfolio, including modernizing legacy software and creating new AI-enabled solutions.
* Establish agentic workflows, developer automation, and shared engineering practices that make software delivery faster, more consistent, and easier to maintain. * Evaluate AI tools, models, and frameworks, selecting the right approach and developing working prototypes that demonstrate measurable value. * Advance the strongest prototypes into secure, reliable production solutions through thoughtful planning, testing, validation, code review, and release practices. *
- Partner with software colleagues and mechanical, electrical, service, and project engineers to solve real product challenges and expand responsible AI adoption across the team .
What We're Looking For
A degree in computer science, software engineering, or a related field, or equivalent practical experience demonstrated through professional work, coursework, or personal projects.
* Strong software engineering skills in one or more modern programming languages, such as TypeScript, Rust, Go, or Python. * Hands-on experience using AI coding tools and agentic workflows to plan, build, test, review, or improve software. * Experience automating engineering or developer workflows through scripting, CI/CD, internal tooling, or similar methods. * The judgment to evaluate LLM output, recognize AI strengths and limitations, and move quickly from experimentation to maintainable, production-ready software. *
Preferred qualifications
Experience building or integrating LLM applications, including retrieval-augmented generation, tool calling, agents, or evaluation frameworks.
* Familiarity with machine learning or using business data to create ML-enabled products. *
- Experience with GitHub Copilot, Claude, Azure, or comparable AI development platforms
What You'll Get from Us
- We promote from within and support your learning with mentoring, training, and access to global opportunities.
- You’ll have flexibility, autonomy, and support to do your best work while maintaining a healthy work-life balance. Your well-being matters to us.
- We strive to create an inclusive work environment where people of all backgrounds are respected, and valued for who they are.
- You’ll receive benefits like annual bonus, paid vacation, pension plans, personal insurance, and more. These vary by country and contract, but they’re worth asking about—we think they’re pretty great.
Ready to Make a Difference?
If this role excites you, we’d love to hear from you! to start the conversation and learn more about where your career can go with us.
Our Commitment to Transparency
Salary ranges listed reflect only primary location and currency. For local salary ranges and specific benefits in your preferred hiring location, please ask your Talent Acquisition representative for more information.
The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience, and location. Any benefits listed do not promise or guarantee any particular benefit or specific action. They may depend on country or contract specifics and are subject to change at any time without prior notice.
Want to know more? Please visit our F AQ Pag e .
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or other protected category.
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 11,520 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Danfoss, 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 1,688 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Danfoss AI Hiring
Danfoss has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Tallahassee, FL, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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