Interested in this AI/ML Engineer role at Xsolla?
Apply Now →Skills & Technologies
About This Role
ABOUT US
Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500\+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game.
For more information, visit xsolla.com.
Why This Role Exists
Xsolla is going all\-in on becoming an AI\-first company, and we're not waiting for a slow rollout to get there. We're offering AI Engineer Internships in regions across the globe to help drive that shift directly, not by sitting in the back office, but by building.
You'll work on two things:
- Internal AI initiatives – helping unlock AI tooling and integrations across Xsolla faster than our existing teams can alone.
- Proof\-of\-concept builds on our own APIs – showing our product and engineering teams what's possible when you build with AI as a core part of your workflow, not an afterthought.
This isn't a shadow\-and\-observe internship. You'll ship code that goes to customers – internal teams or external players and game developers – working closely with senior engineers and fellow interns across our global regions.
Who You'll Work With
You won't be isolated on an intern\-only project island. You'll work closely with senior engineers who are actively rebuilding how we write software, and you'll be part of a global cohort of interns building in parallel across our regions. That combination is the point: senior engineers give you a bar for what production\-grade, customer\-facing code looks like, and your fellow interns give you a peer group to trade ideas, code, and AI workflows every week.
### What You'll Do
- Partner and collaborate with senior engineers to identify where AI tooling and integrations can remove friction, and build the fix.
- Build applications and integrations on top of Xsolla's APIs and SDKs that show internal teams how to move faster using AI\-assisted development.
- Ship work that matters. What you build is expected to reach real users – whether that's an internal tool an engineering team adopts or a customer\-facing integration – not a throwaway exercise that gets shelved after your internship ends.
- Use agentic coding tools (Claude Code, Cursor, or similar) as your default way of working – not a novelty you reach for occasionally.
- Share what you build. We run weekly 30\-second lightning\-talk demos where everyone shows how they used AI to solve something that week – you'll be expected to bring something real.
- Work with minimal process. You'll get context, a problem, and a mentor – the rest is on you to figure out and build.
- Learn from and collaborate with both directions: senior engineers who'll mentor you on production\-grade practices, and fellow interns across markets you'll trade ideas and code with at the weekly demos.
### What We're Looking For
You build things because you want to, and that you already use AI to do it faster.
- Genuine passion for building. You have side projects, hackathon entries, open\-source contributions, or something you shipped just because you were curious. You didn't need an assignment to make it happen.
- Real AI fluency. You've actually used tools like Claude Code, Cursor, Copilot, or similar agentic coding tools to build something – not just asked ChatGPT to explain a concept. You should be able to explain how you used AI to get there and how you confirmed it actually worked – not someone who fires off a prompt and hopes for the best.
- Bias toward action. Given an ambiguous problem, you start building before you ask for a spec.
- Comfort with developing modern full\-stack solutions – we care more about how fast you learn than what you already know cold.
- Open to current undergraduate students, graduate students, and recent graduates of a Computer Science, Software Engineering, or related program. If you're pursuing a graduate degree and don't mind taking on an internship\-level role to get hands\-on with AI, we want to hear from you.
### How to Apply
No take\-home assignments, no formal problem sets. We're evaluating proof of work, not your ability to complete an assigned exercise. Please include:
- A link to a GitHub profile, deployed project, or anything else you've built – the more recent and self\-directed, the better.
- A short answer (2\-3 sentences, or a 2\-minute video/Loom if you'd rather show than tell) to: “What's something hard you built recently, and why did you build it?”
We're not grading for correctness or polish. We're looking for whether you have a real answer, and whether it's clear you actually did the work yourself (with AI as your tool, not your ghostwriter).
Equal Employment Opportunity Statement:
Xsolla is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or any other characteristic protected by law.
We consider qualified applicants with criminal histories in accordance with the Fair Chance Act.
Criminal History Consideration:
For the AI\-First Engineering Intern, we will conduct a background check that may include the following:
Criminal history check
Employment verification
Education verification
Relevance to Job Responsibilities:
The background check is relevant to this position because of the following role responsibilities:
Accessing confidential company data
Ensuring compliance with regulatory requirements
Rights Under the Fair Chance Act:
Applicants are encouraged to inquire about their rights under the Fair Chance Act. If you have questions regarding our hiring practices, please contact [email protected].
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $41K-$52K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Xsolla, 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($46K) sits 79% below the category median. Disclosed range: $41K to $52K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Xsolla AI Hiring
Xsolla has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Raleigh, NC, US. Compensation range: $52K - $52K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.