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About This Role
Join LearnWise
LearnWise is a North America\-based career coaching platform dedicated to helping students secure internships and full\-time positions at leading technology companies. Our mentor network includes experienced engineers from Meta, Google, Amazon, Microsoft, NVIDIA, Apple, OpenAI, and other top technology companies.
We are currently seeking an experienced AI Engineer Mentor to provide one\-on\-one technical coaching for students pursuing careers in Artificial Intelligence, Machine Learning, and Generative AI.
This is a remote, part\-time contract position with flexible scheduling based on mentor availability.
Position Overview
As an AI Engineer Mentor, you will work directly with students through personalized one\-on\-one coaching sessions. You will help students strengthen their technical foundations, build competitive AI projects, prepare for technical interviews, optimize resumes, and successfully navigate the North American recruiting process.
This position is ideal for experienced AI engineers who are passionate about mentoring and enjoy helping aspiring engineers achieve their career goals.
Key ResponsibilitiesOne\-on\-One Technical Coaching
- Deliver personalized one\-on\-one mentoring sessions tailored to each student's technical background and career goals.
- Develop customized learning plans covering AI fundamentals, interview preparation, and project development.
Technical Interview Preparation
- Conduct coding interviews and AI/ML mock interviews.
- Provide interview coaching for AI Engineer, Machine Learning Engineer, Applied Scientist, and Software Engineer (AI) positions.
- Help students improve problem\-solving, communication, and technical interview performance.
AI Project Mentorship
- Guide students through production\-oriented AI projects suitable for resumes and interviews.
- Review project architecture, implementation, and code quality.
- Help students build practical experience with modern AI technologies.
Resume \& Career Coaching
- Review and optimize technical resumes.
- Provide personalized career advice and job search strategies.
- Share insights into North American hiring processes and interview expectations.
Student Progress
- Track student progress throughout the program.
- Collaborate with Student Advisors to ensure students stay on schedule and receive timely support.
QualificationsEducation
- Bachelor's degree or above in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a closely related field.
Professional Experience (Required)
- Minimum 3 years of full\-time engineering experience at a leading technology company in North America.
- Professional experience as an AI Engineer, Machine Learning Engineer, Applied Scientist, AI Research Engineer, or Software Engineer specializing in AI/ML.
- Proven experience designing, developing, and deploying production\-grade AI applications.
- Hands\-on experience building commercial AI products rather than academic research only.
- Experience participating in technical interviews at North American technology companies.
Technical Skills (Required)
Candidates should have strong hands\-on experience with most of the following:
- Python
- SQL
- Machine Learning
- Deep Learning
- Large Language Models (LLMs)
- Prompt Engineering
- Retrieval\-Augmented Generation (RAG)
- AI Agent Development
- LangChain / LangGraph / LlamaIndex
- OpenAI API, Anthropic API, Gemini API, or similar LLM platforms
- PyTorch and/or TensorFlow
- Git
Experience with the following is considered a strong asset:
- MLOps
- Docker
- Kubernetes
- AWS, Azure, or Google Cloud
- Hugging Face
- Vector Databases (FAISS, Pinecone, Weaviate, Milvus)
- Model Fine\-tuning and Deployment
Communication \& Mentoring
- Excellent communication and interpersonal skills.
- Ability to explain complex technical concepts clearly to students with different technical backgrounds.
- Passion for mentoring and helping students succeed in the North American job market.
Work Authorization (Required)
- Must be legally authorized to work in the United States.
- Candidates must currently hold valid U.S. work authorization.
Language Requirements
- Fluent in English.
- Mandarin Chinese (written and spoken) is required, as the majority of our students are native Mandarin speakers.
Pay: $120\.00 \- $300\.00 per hour
Work Location: Hybrid remote in Bellevue, WA 98005
Salary Context
This $249K-$624K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At LearnWise Solutions Inc., 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($436K) sits 103% above the category median. Disclosed range: $249K to $624K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
LearnWise Solutions Inc. AI Hiring
LearnWise Solutions Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bellevue, WA, US. Compensation range: $624K - $624K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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