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About This Role
Get paid to help build better AI.
Alpheva AI is looking for experienced software engineers to participate in paid, project-based work that helps train and evaluate the next generation of AI coding systems.
This is not a full-time job. You work remotely and asynchronously on projects that fit around your existing schedule.
Your engineering experience becomes the signal that helps AI systems learn what good software engineering actually looks like.
What you'll do
Depending on the project, you may:
- Build: Solve realistic software engineering problems and produce high-quality implementations.
- Evaluate: Review AI-generated code, pull requests, architectures, tests, and debugging approaches.
- Debug: Diagnose failures in real or realistic software repositories and explain what went wrong.
- Review: Assess whether an AI agent's implementation is correct, maintainable, secure, and production-ready.
- Demonstrate: Complete software engineering workflows in real development environments so AI systems can learn how experienced engineers actually work.
- Create evaluations: Develop challenging engineering tasks and criteria for measuring AI coding agents.
- Compare solutions: Assess multiple AI-generated solutions and identify meaningful differences in correctness, quality, and engineering judgment.
Projects may involve repository navigation, implementation, testing, debugging, code review, system design, and other software engineering workflows.
Who we're looking for
We're looking for experienced working software engineers, not necessarily AI researchers.
You should have:
- 3+ years of professional software engineering experience
- Strong programming and debugging skills
- Experience working in production codebases
- Ability to understand unfamiliar repositories and systems
- Strong code review and technical judgment
- Experience writing and reviewing tests
- Ability to clearly explain why a solution is correct or incorrect
Experience with any of the following is valuable:
- Python, JavaScript/TypeScript, Go, Java, C++, Rust, or similar
- Backend or distributed systems
- APIs and microservices
- Databases and data systems
- Cloud infrastructure
- DevOps / CI/CD
- Security
- Frontend engineering
- Mobile development
- Machine learning infrastructure
- Agentic AI or coding agents
You do not need prior experience working in AI.
What matters most is that you know how to do software engineering well.
What the work looks like
You might be asked to:
Fix a bug in an unfamiliar repository.Review an AI-generated pull request and identify the issues.Implement a feature and write the appropriate tests.Compare two AI-generated implementations and determine which actually works.Debug a failing application and explain the root cause.Evaluate whether an AI coding agent successfully completed an engineering task.Work through a multi-step software engineering task in a real development environment.
The goal is not simply to determine whether code looks good.
The goal is to capture the judgment an experienced engineer uses to determine whether software actually works.
Compensation
Paid project-based work.
Rates vary based on your experience, specialization, and project requirements. Your applicable rate and project scope will be shown before you commit to a project.
Some projects are short assignments. Others may involve recurring work over a longer period.
There is no fixed schedule and no minimum commitment.
Work arrangement
- Remote
- Asynchronous
- Project-based
- Flexible hours
- Work around your existing job or commitments
- Paid for qualifying project work
Why Alpheva
AI coding systems are becoming remarkably capable at generating code.
But writing a plausible-looking patch is not the same as being a good software engineer.
AI needs to learn how experienced engineers:
- Understand unfamiliar systems
- Make architectural tradeoffs
- Debug failures
- Write reliable tests
- Review code
- Handle edge cases
- Reason about security and performance
- Decide when an implementation is actually production-ready
That's where you come in.
Your engineering judgment helps shape how AI learns to build software.
How it works
1. Apply
Tell us about your engineering background, experience, and areas of expertise.
2. Get qualified
Complete a short, paid software engineering assessment designed around the type of work you'll perform.
3. Get matched
When your experience matches an active project, we'll share the scope, requirements, and compensation.
4. Do the work
Complete projects remotely and asynchronously.
5. Get paid
Receive the agreed rate for your completed work.
Important
You are not required to share confidential information, proprietary source code, trade secrets, or materials belonging to your employer.
Projects are designed so that your professional expertise can be applied without exposing confidential employer information.
Help teach AI how great engineers actually work.
Apply to become an Alpheva Software Engineering Expert. check out - https://alpheva.ai/experts/
Pay: From $300,000.00 per year
Work Location: Remote
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 Alpheva AI, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
Alpheva AI AI Hiring
Alpheva AI has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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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