Interested in this AI/ML Engineer role at Xsolla?
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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.
### Responsibilities
This is a hands\-on builder role from day one. You will write code, build pipelines, and ship automation every week. This is not a strategy\-only position.
- Own the Intelligence and Automation function for GSIP and Web3 PS — design, build, and maintain automated workflows (n8n or similar) for meeting notes processing, trip reports, intake routing, and reporting
- Develop and maintain integrations across Salesforce, Jira, Confluence, Atlas, and Neo4j to create a unified intelligence layer
- Design and build executive dashboards that surface real\-time portfolio health, deal pipelines, partnership progress, and KPIs for leadership across both divisions
- Build and maintain Confluence\-based intelligence pages — partner profiles, initiative trackers, competitive intelligence, and automated content pipelines
- Support the company's operating framework that separates strategic narrative, operational process, and intelligence/automation — building workflows around stage gates, milestone tracking, approvals, and templates
- Drive AI adoption across both divisions, identifying opportunities to increase operational efficiency through Claude, Neuronet, and other AI tools
- Own the Technical Strategy Roadmap for GSIP and Web3 PS, setting the long\-term vision for automation and intelligence infrastructure
- Establish cadences for weekly reporting, monthly optimization reviews, and quarterly ROI reporting
- Measure and communicate the leverage gained through technology investments
- Continuously scout emerging AI capabilities, models, and tools on a weekly cadence. Run rapid experiments and present findings to the team
- Conduct regular demo sessions and hands\-on training to ensure every team member across both divisions can effectively leverage AI tools. Lead by showing, not telling
- Attend key GSIP and Web3 PS meetings and working sessions to deeply understand operational context. Solutions must emerge from firsthand knowledge of how the team works
- Once automation is validated, hand off to operations leadership for integration into standard operating workflows. You pioneer; they scale
- Establish and maintain AI governance practices — ensuring AI decisions are traceable, compliant, and reversible
- Build predictive models for deal outcomes, partnership health, and initiative success. Surface anomalies and patterns before they become problems
### Sample Success Metrics
- Automation coverage percentage — share of cross\-divisional workflows with automation vs. manual execution
- Manual effort reduction — measurable hours saved per week/month through automation
- Cycle time compression — faster turnaround on reporting, meeting notes, intake processing, and partner intelligence
- Leverage ROI — demonstrable return on technology investments relative to time and cost invested
- Dashboard adoption — percentage of leadership actively using intelligence dashboards for decision\-making
- AI\-assisted quality improvement — reduction in errors, rework, and inconsistencies through automated validation
### This Role is NOT
- A tool collector — adopting every shiny new AI tool without measuring impact
- IT support — this is a strategic builder role, not a help desk
- A disconnected experiment lab — you must be embedded in the team's daily reality
- A process designer — operations leaders own workflow design; you automate within their frameworks
- A pure data science role — you build production systems that deliver daily value, not research models
- Disqualifiers: "AI will solve everything" mentality, tool\-first thinking without business context, inability to measure impact quantitatively.
### What a Great Week Looks Like
- Monday: Scout 3 new AI capabilities released that week
- Tuesday: Demo a prototype automation to the team
- Wednesday: Ship an integration that eliminates 2 hours of manual work
- Thursday: Present a dashboard insight that changes a leadership decision
- Friday: Hand off a validated automation to operations leadership for scaling
### Qualifications \& Skills
### Required Qualifications
- 3\+ years of experience in technical operations, business intelligence, automation engineering, or a related field
- Pragmatic AI/automation mindset — you focus on measurable leverage, not hype
- Strong hands\-on experience building automation workflows (n8n, Zapier, Make, or custom\-built pipelines) with a track record of eliminating manual work at scale
- Proficiency in at least one programming language (Python, Node.js/JavaScript, or TypeScript) with ability to write production\-quality scripts and integrations
- Systems integration experience — connecting multiple enterprise platforms (CRMs, project management, content systems) into unified data flows
- Experience designing and building executive dashboards that communicate complex data clearly to leadership audiences
- Working knowledge of the Atlassian suite (Jira, Confluence, Atlas) and CRM systems (Salesforce preferred)
- Excellent documentation and communication skills
- Self\-directed and proactive — you identify gaps, propose solutions, and execute without waiting to be told
- Understanding of AI limitations — you know when automation is the wrong answer and when human judgment must remain in the loop
### Preferred Qualifications
- Experience in the gaming industry or with game publishers/studios
- Familiarity with graph databases (Neo4j) and knowledge graph concepts
- Experience with AI/ML tools and platforms in an applied business context (e.g., Claude, GPT, LLM\-based automation)
- Background in NPI (New Product Introduction) frameworks or stage\-gate processes
- Experience with data visualization tools (Looker, Grafana, Metabase, or custom React dashboards)
- Experience deploying applications to cloud platforms (Netlify, Railway, Render, Fly.io, or similar)
- Bachelor's degree in Computer Science, Information Systems, Business Analytics, or a related field (or equivalent practical experience)
Benefits:
We are passionate about fostering a supportive environment for our team, so we prioritize the physical, mental, and emotional well\-being of our employees and their families through a comprehensive Benefits Program. This includes 100% company\-paid medical, dental, and vision plans, unlimited Flexible Time Off, and a personalized career roadmap for each employee. By investing in professional development through training and educational opportunities, we ensure that our team thrives both personally and professionally. Together, we’re not just building a business; we’re cultivating a community that values creativity, collaboration, and the transformative power of play. *By submitting the following job application form, you consent to Xsolla processing your data for career\-related inquiries and potential employment opportunities. We process your data in accordance with this* *Xsolla Privacy Notice for Job Applicants**. Please direct any inquiries regarding your data privacy to [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. 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 $110K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($125K) sits 30% below the category median. Disclosed range: $110K to $140K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Xsolla AI Hiring
Xsolla has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $77K - $140K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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