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About This Role
Science & Engineering – AI Trainer
Remote | Independent Contractor | $40–$125/hr USD
About the Role
We are seeking experienced scientists, engineers, and technical professionals to evaluate how advanced artificial intelligence models perform real-world scientific and engineering tasks.
This opportunity allows professionals with backgrounds in mathematics, physics, chemistry, biology, engineering, or data science to apply their expertise to improving AI-generated technical solutions.
Projects may involve analyzing experimental data, evaluating engineering designs, interpreting simulations, reviewing scientific calculations, investigating technical failures, and assessing research methodologies.
In this role, you will develop realistic technical challenges based on your professional experience, evaluate AI-generated responses, identify errors, and provide detailed feedback to improve model accuracy and reasoning.
This is not a traditional science or engineering position. Instead, you will apply your technical knowledge and analytical judgment to help advance AI technology in a flexible, fully remote environment.
Benefits of the Opportunity
- Work remotely using your own computer.
- Flexible scheduling with the ability to select projects based on your availability and expertise.
- Competitive compensation ranging from $40–$125 USD per hour, depending on project requirements.
- Opportunities to work on challenging scientific and engineering projects.
- Access to required AI tools without needing personal subscriptions.
- Opportunities to contribute across multiple technical disciplines.
Responsibilities
- Develop challenging, realistic scientific and engineering tasks based on professional experience, including technical calculations, experimental designs, data analysis, engineering evaluations, and technical reporting.
- Create supporting materials such as datasets, laboratory records, technical specifications, drawings, schematics, simulation outputs, and reports.
- Evaluate AI-generated technical solutions for accuracy, completeness, and adherence to professional standards.
- Compare responses from different AI models, identify strengths and weaknesses, and document areas for improvement.
- Develop detailed evaluation rubrics and success criteria to determine whether AI-generated solutions meet technical requirements.
- Identify technical errors, including incorrect calculations, unit conversions, flawed assumptions, statistical mistakes, unsupported conclusions, and overlooked safety considerations.
- Review experimental methodologies, measurement uncertainty, engineering tradeoffs, and technical interpretations.
- Collaborate with other technical experts to review and refine evaluation tasks.
Qualifications
- 2+ years of applied professional experience preferred in a scientific, engineering, or data science discipline.
- Bachelor's degree in progress or completed in a relevant field.
- Professional experience in one or more of the following areas:
- Mathematics, Physics, Chemistry, or Biology
- Electrical or Electronics Engineering
- Civil or Structural Engineering
- Mechanical or Materials Engineering
- Chemical or Process Engineering
- Environmental or Earth Sciences
- Data Science or Applied Statistics
- Related scientific or technical disciplines
- Hands-on experience working with real-world technical data, experimental results, simulations, or engineering documentation.
- Familiarity with relevant technical tools such as Python, MATLAB, R, SPICE, CAD, FEA, CFD, GIS, or laboratory analysis software.
- Understanding of statistical analysis, experimental design, measurement accuracy, and scientific reasoning.
- Ability to identify technical errors and clearly explain findings in writing.
- Current or recent hands-on experience performing scientific or engineering work.
- Strong written and verbal English communication skills.
- Familiarity with AI tools such as ChatGPT or Claude, or willingness to learn.
- Comfortable using cloud-based collaboration tools, desktop applications, and common file formats.
- Programming or scientific computing experience is strongly preferred and required for certain projects.
- Advanced degrees or professional engineering licenses are a plus but not required.
Compensation and Contract Details
- Pay: $40–$125 USD per hour, depending on project and qualifications.
- Position Type: Independent contractor.
- Location: Fully remote.
- Schedule: Flexible, with a stated minimum of 10 hours per week and no weekly maximum. Some projects prioritize availability of 30–40 hours per week.
- Payment Method: PayPal, on a regular payment schedule.
- Eligible Locations: United States, Canada, and United Kingdom.
- Tools: Access to required AI platforms is provided.
Application and Assessment Process
- Apply: Submit your application and complete the required account registration.
- Skills Assessment: Complete an unpaid technical assessment lasting approximately 2–4 hours. The assessment evaluates scientific reasoning, technical judgment, and the ability to identify errors in AI-generated work. All assessment submissions must be completed independently without AI assistance.
- Assessment Review: Qualified submissions are reviewed by technical evaluators. Candidates who successfully pass will be invited to complete onboarding.
- Project Training: Complete introductory training covering task development, supporting documentation, and evaluation criteria.
- Task Development: Propose realistic technical challenges based on professional experience and create supporting materials.
- Evaluation and Feedback: Submitted work undergoes expert review, with opportunities for feedback, revisions, and additional assignments.
Project participation and compensation are subject to applicable review and acceptance requirements. Passing the assessment does not guarantee immediate project availability.
About the Opportunity
This opportunity connects experienced science and engineering professionals with projects focused on evaluating and improving advanced artificial intelligence systems.
Your real-world technical expertise will help identify limitations in AI-generated scientific and engineering solutions, improve model reasoning, and support the development of more reliable AI technologies.
If you have a strong scientific, engineering, or data science background and are interested in applying your expertise to emerging AI technology, we encourage you to apply.
Pay: $40.00 - $125.00 per hour
Benefits:
- Flexible schedule
Application Question(s):
- Which field best represents your professional experience?
- Have you professionally analyzed experimental data, performed technical calculations, evaluated engineering designs, or interpreted scientific results?
- Are you willing to complete an unpaid, independent technical assessment lasting approximately 2–4 hours without using AI assistance?
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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