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About This Role
Who you are
As a Copia employee, you’ll solve challenging, unique, and meaningful problems alongside passionate coworkers while we change the way everything gets built.
As a Senior Platform Engineer, you bring experience across Site Reliability Engineering, DevOps, and Infrastructure Engineering. You take ownership of technical projects (ideally with prior experience solving similar problems) and provide thought leadership. You’re comfortable making architectural decisions, guiding other engineers, and helping set the technical direction of the product.
Building products for the industrial automation space requires knowledge uncommon to most software engineering jobs. In your day\-to\-day, you’ll learn about a wide variety of industrial automation topics.
About us
Copia Automation provides best\-in\-class disaster and recovery technology for industrial automation.
Our product brings modern developer tools to the OT space through our novel Git\-based source control for automation professionals, with multiple delivery methods including self\-hosted and fully hosted options. We are a well\-funded startup with a growing list of happy customers throughout the industrial space.
As a Copia employee, you’ll solve challenging, unique, and meaningful problems alongside passionate coworkers, while we change the way everything gets built.
Why industrial automation?
Manufacturing is powered by industrial automation—computerized systems and robotics running on PLCs. This ecosystem uses a unique graphical language that doesn’t work with typical developer tools like GitLab or GitHub. Instead, automation professionals rely on physical storage devices, incompatible tools, and incomplete solutions. When minutes of downtime can cost tens of thousands of dollars, better development tools are urgently needed.
Engineering at Copia
The Copia engineering team solves problems through collaboration, experimentation, and ownership. We pair on tough issues and look for ways to test our assumptions. You’ll join a team that encourages ownership and values pragmatic solutions, weighing short\-term needs and long\-term goals.
Finally, we’re optimistic. We celebrate successes, support each other through unexpected challenges, and approach problems with a positive attitude.
### What you'll do:
- Ship full\-stack features end to end, handling everything from the backend data models to the final user experience.
- Make AI coding agents a central part of how you work to increase your velocity while maintaining high standards.
- Drive the technical direction of your projects, including architectural decisions and technical specs.
- Collaborate with Product and Design to scope out work, balancing quick wins with long\-term stability.
- Write code that is easy to scale and maintain, and help raise the quality of the codebase through thoughtful reviews and testing.
- Teach the team how to better leverage AI tools by sharing what you have learned and setting best practices.
- Write clear documentation for both technical and non\-technical audiences.
- Guide and mentor other engineers, helping them improve their skills and ship code with confidence.
- Talk directly with customers and executives to turn complex industrial challenges into clear engineering tasks.
### What you'll need:
- 5\+ years of professional software engineering experience shipping production software on a team using standard best practices (code review, CI/CD, testing).
- A strong full\-stack background, with the ability to build polished frontends and robust backend services.
- Daily experience with AI coding tools like Cursor, Claude Code, or Copilot, and a good sense of their strengths and weaknesses.
- Deep knowledge of at least one statically\-typed language. We use Go, TypeScript, and C\#, but we care more about your engineering depth than the specific language.
- A history of delivering complex projects on time and the ability to explain the technical choices you made along the way.
- Great instincts for prioritization and the ability to take an ambiguous problem and find a clear solution.
- Comfort making architectural decisions and providing technical guidance to other engineers.
- Excellent communication skills, with the ability to explain technical details to both engineers and non\-technical stakeholders.
- Eagerness to dig into a complex domain. You don't need to know PLCs on day one, but you should be excited to learn how factories actually run.
- Previous experience adding AI\-powered features into a product
### Nice to haves:
- Experience at a growth\-stage, venture\-backed startup, scaling tools from small betas to thousands or millions of users.
- Experience with our stack or adjacent technologies: TypeScript / Node.js, React, Go, C\#, Python, PostgreSQL, AWS.
- Experience building developer tools or working on Git internals, CI/CD, or DevOps platforms.
- Background in industrial automation, OT/IT, manufacturing software, IoT, or developer tooling.
- Interest in manufacturing and hardware innovation.
- Competitive base salary commensurate with experience and location.
- Meaningful equity in an early\-stage, venture\-backed company.
- Comprehensive medical, dental, and vision coverage.
- Unlimited PTO and company holidays.
- 401(k) plan.
- Commuter benefits and in\-office lunches at our NYC HQ.
- Hybrid work environment with relocation assistance available for qualifying candidates.
- A high\-trust, high\-ownership culture where senior engineers are expected to lead technically, ship, and grow the people around them.
Copia is an equal opportunity employer. We are committed to building a diverse team and an inclusive culture, and we welcome applicants from all backgrounds. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.
Salary Context
This $172K-$226K range is above the median for AI Software Engineer roles in our dataset (median: $190K across 193 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 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Copia Automation, 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 $234,620 based on 682 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($199K) sits 15% below the category median. Disclosed range: $172K to $226K.
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.
Copia Automation AI Hiring
Copia Automation has 1 open AI role right now. They're hiring across AI Software Engineer. Based in New York, NY, US. Compensation range: $226K - $226K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% 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 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
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