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About This Role
Software Engineer – AI Trainer
Contract | Remote | $40–$125/hr USD
About the Role
We are seeking experienced software engineers to evaluate how AI coding agents handle real engineering work inside realistic codebases: implementing changes to specifications, fixing bugs with regression tests, refactoring without breaking behavior, reviewing pull requests, and debugging from traces.
AI coding agents are strong in many ways but still struggle with edge cases, brittle changes, and complex engineering decisions. This opportunity allows experienced engineers to apply their professional judgment to evaluate and improve AI-generated work.
In this role, you will design challenging, realistic tasks drawn from your own professional experience, run them through advanced AI agents, and evaluate the results against professional engineering standards.
You will work with realistic professional files and codebases that you assemble yourself. Some tasks are compact, while others involve larger scenarios developed over multiple days.
This is not a traditional software engineering role. You will help improve AI systems by applying your technical expertise in a structured, flexible, fully remote environment.
Responsibilities
- Design challenging, realistic software engineering tasks based on professional experience, including bug fixes, feature development, refactoring, code reviews, debugging, and API development.
- Create supporting materials such as code repositories, tests, logs, API contracts, and technical documentation.
- Run tasks through AI coding agents and evaluate their output against professional engineering standards.
- Compare AI-generated solutions, identify strengths and weaknesses, and document areas for improvement.
- Develop tests, evaluation rubrics, and success criteria to determine whether AI-generated solutions meet requirements.
- Identify technical failures, including missed edge cases, brittle implementations, incorrect tests, and deviations from specifications.
- Review and refine tasks developed by other engineering professionals.
Qualifications
- 2+ years of professional software engineering experience preferred, including shipping and maintaining production code.
- Bachelor's degree in progress or higher.
- Proficiency in at least one programming language or stack, including TypeScript, JavaScript, Python, Java, Go, Rust, C++, C#, Ruby, PHP, Swift, or Kotlin.
- Frontend, backend, and mobile engineering specialists are encouraged to apply.
- Comfortable working with Git, testing frameworks, debugging tools, and unfamiliar codebases.
- Experience with Docker and CI/CD is a plus.
- Strong technical judgment and the ability to clearly explain engineering decisions in writing.
- No prior AI or machine learning experience required.
- Current or recent hands-on experience as an individual contributor.
- Full professional or native-level written and spoken English proficiency.
- Familiarity with AI tools such as Claude or ChatGPT.
- Comfortable with cloud-based collaboration tools, desktop applications, and general file management.
- Computer science degree preferred but not required; relevant professional experience is highly valued.
Compensation and Terms
- Compensation: $40–$125 per hour USD.
- Employment Type: Independent contractor.
- Location: Fully remote.
- Schedule: Flexible, with a minimum of 10 hours per week and no weekly maximum. Full-time hours may be available.
- Payment: Regular payments through PayPal.
- Tools: Access to required AI tools is provided.
- Eligibility: Candidates must be based in the United States, Canada, or the United Kingdom.
Application and Assessment Process
- Apply: Submit your application and complete the required account registration.
- Skills Assessment: Complete an unpaid assessment lasting approximately 2–4 hours. The assessment evaluates technical expertise, critical thinking, and the ability to assess AI-generated work. All submissions must be completed independently without AI assistance.
- Review and Onboarding: Candidates who successfully pass the assessment will be invited to complete onboarding and access available projects.
- Project Training: Complete introductory training covering task development, supporting files, and evaluation criteria.
- Task Development: Propose realistic engineering tasks based on professional experience and develop supporting materials.
- Evaluation and Feedback: Submitted work undergoes expert review, with feedback and potential revisions throughout the process.
Each stage is subject to review and approval before progressing. Compensation applies to eligible, accepted paid project work.
About the Opportunity
This opportunity connects experienced software engineers with projects focused on evaluating and improving advanced artificial intelligence systems.
By applying real-world engineering expertise, contributors help identify limitations in AI-generated code, improve model performance, and support the development of more reliable AI technologies.
If you have a strong software engineering background and are interested in applying your skills to emerging AI technology, we encourage you to apply.
Pay: $40.00 - $125.00 per hour
Benefits:
- Flexible schedule
Application Question(s):
- Do you have at least 2 years of professional, hands-on software engineering experience developing and maintaining production code?
- Which programming languages do you have professional experience using?
- Are you willing to complete an unpaid, independent technical assessment lasting approximately 2–4 hours as part of the qualification process?
Work Location: Remote
Salary Context
This $83K-$260K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Paro, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($171K) sits 19% below the category median. Disclosed range: $83K to $260K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Paro AI Hiring
Paro has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $260K - $260K.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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