Interested in this AI/ML Engineer role at TripleTen?
Apply Now →About This Role
Nebius Academy is an educational platform that helps experienced software developers stay at the forefront of technological innovation. We create specialized courses that teach professionals how to effectively use AI tools in real\-world development workflows.
Our programs are designed to help engineers critically and productively apply artificial intelligence in their professional work, improving code quality, development speed, and overall work efficiency.
Our mission: Provide engineers with access to cutting\-edge knowledge in AI\-assisted development and help them become leaders in the new era of programming.
We're building a hands\-on workshop series for experienced QA engineers on the practical use of AI\-assisted testing tools in real\-world QA workflows. As the Expert Instructor, you will lead interactive live sessions that go beyond passive learning, helping QA engineers integrate AI tools into their day\-to\-day practice.
Your audience will be B2B clients: skilled QA professionals working in fintech environments — payments, banking, card systems, and adjacent domains — focused on improving coverage, efficiency, and testing quality through AI\-assisted workflows.
We are seeking experienced developers and technical experts (5\+ years) with hands\-on AI tools experience, a proven track record in tech leadership or mentoring, and the ability to explain complex concepts clearly. Ideal candidates are fluent in English, detail\-oriented, able to work independently, and can commit 10\-15 hours per week to creating and/or presenting educational content for professional developers.
Please submit your resume in English.
Requirements:
- 5\+ years of professional experience in QA engineering;
- Hands\-on experience with AI\-assisted QA tools and proven cases of their successful implementation;
- Comfort with test strategy, automation approaches, and modern QA tooling in high\-compliance, high\-reliability environments;
- Background in engineer advocacy, tech leadership, conference participation, or mentoring.
- Ability to translate complex QA and fintech concepts into actionable, engaging learning experiences;
- Russian language (some of our students speak Russian);
- Fluent spoken and written English;
- Confident, collaborative, and audience\-oriented facilitation style;
- Strong attention to detail and ability to work independently;
- Time commitment: able to devote \~10 hours per week.
Nice to have:
- Background in fintech QA (payments, banking, card systems, or adjacent domains);
- Experience teaching or coaching senior QA engineers or engineering teams in fintech companies;
- Familiarity with remote collaboration tools (Zoom, Miro, Slack, etc.);
- Personal experience as an online learner;
- Interest in instructional design and education.
Brand:
Nebius Academy
What you will do:
- Conduct live, interactive training sessions and workshops;
- Create clear, concise content for lessons, manuals, and assessments — including texts, draft slides, and screencasts;
- Participate as a speaker in learning videos and design the course's final project;
- Review and incorporate learner feedback to continuously improve session and content design;
- Work with the curriculum team to ensure strong alignment between async and live content;
- Communicate with students during Q\&A sessions.
- This role requires 10\-15 hours of availability per week.
What we can offer you:
- The opportunity to create impactful content while maintaining your primary job: Share your expertise without leaving your current role
- Competitive hourly rate of $40\-$80 USD for flexible part\-time collaboration with significant impact and an amazing team!
- Remote cooperation with a schedule convenient for both you and the team: We don't focus on micromanagement
- Cross\-cultural experience: Become part of an international team and connect with professionals from diverse backgrounds
- Meaningful impact: Share your knowledge and help experienced engineers advance their skills through high\-quality educational content
- Participation in innovative projects: Contribute to shaping the future of programming education and AI adoption
- Professional growth: Receive feedback and develop your skills as a technical content creator and thought leader
Salary Context
This $83K-$166K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At TripleTen, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($124K) sits 31% below the category median. Disclosed range: $83K to $166K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
TripleTen AI Hiring
TripleTen has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $166K - $208K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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