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About This Role
AI / LLM Systems Engineer – Egofold (Junior to Mid\-Level)
Location: Culver City, CA
Compensation: $110,000 – $150,000 Annually
About Snail Games USA
Snail Games strives to create the new high bar for gameplay experience in online gaming. We have been a global developer and publisher of digital entertainment since 2009 and are committed to pushing the boundaries of the industry.
About Egofold
Egofold is an AI initiative within Snail Games focused on building a modular AI "brain" ecosystem for NPC intelligence, real\-time perception systems, and simulation tooling across multiple game projects.
About the Role
We are seeking a Junior to Mid\-Level AI / LLM Systems Engineer to help build and expand Egofold's foundational AI systems. This role will contribute to the development of reusable, trainable LLM\-based systems that support intelligent agents operating across multiple environments and use cases.
You will work closely with senior engineers and technical leadership to implement AI architectures, develop training and evaluation workflows, and integrate AI capabilities into real\-time applications. This is an excellent opportunity for someone who has hands\-on experience with modern AI technologies and wants to grow their expertise while contributing to a long\-term AI platform vision.
Job Type
Full\-Time
Location
Hybrid – Los Angeles Area (3–4 in\-office days per week)
Responsibilities
- Assist in designing and implementing AI systems that leverage large language models for reasoning, decision\-making, and agent behaviors.
- Support the development of training, evaluation, and feedback workflows for AI agents and LLM\-powered systems.
- Build tools and processes for testing, benchmarking, and monitoring AI performance.
- Help implement memory, context management, and world\-state representation systems within AI architectures.
- Develop validation and safety mechanisms to improve reliability and consistency of AI outputs.
- Collaborate with senior engineers to integrate AI systems into real\-time applications and interactive environments.
- Contribute to experimentation and prototyping efforts involving agent frameworks, retrieval systems, and AI workflows.
- Participate in technical discussions, code reviews, and architecture planning activities.
- Document systems, workflows, and technical decisions to support team collaboration and future development.
Minimum Requirements
- 2–5 years of professional software engineering, machine learning, or AI development experience.
- Experience working with large language models through APIs, open\-source models, fine\-tuning, prompt engineering, RAG systems, or agent frameworks.
- Strong programming skills in Python.
- Familiarity with machine learning concepts, model evaluation, and AI development workflows.
- Experience building, integrating, or deploying software systems in production environments.
- Understanding of software engineering best practices including testing, version control, and documentation.
- Strong analytical and problem\-solving skills.
- Ability to learn quickly and thrive in a fast\-paced, startup\-like environment.
Nice to Have
- Experience with agent frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar technologies.
- Exposure to reinforcement learning, simulation environments, or agent training methodologies.
- Experience with vector databases, retrieval systems, and embedding models.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
- Experience integrating AI systems into games, simulations, or interactive applications.
- Knowledge of C\+\+, C\#, Unreal Engine, or Unity.
- Contributions to open\-source AI or machine learning projects.
Salary Range
$110,000 – $150,000 annually
Why Join the Snail Games USA Team?
Operate in a small, high\-autonomy team with significant technical ownership and long\-term influence.
True focus on work/life balance
Paid company holidays, vacation, and separate sick leave
Medical, dental, vision, and Life/LTD
401k with company match
Work Authorization Requirements
Applicants must be legally authorized to work in the United States at the time of application. This position does not offer visa sponsorship now or in the future (including H\-1B).
Additional Information
As part of the Company’s activities in video game development, publishing, and short\-form video content creation, certain projects, discussions, or creative materials may include themes, visuals, language, or subject matter that some individuals could find mature, violent, sexual, graphic, or otherwise sensitive in nature (collectively referred to as “Mature Content”). Examples may include, but are not limited to, depictions or descriptions of combat, violence, adult themes or relationships, suggestive or satirical humor, or strong language. Employees are expected to engage with such material in a professional and creative context as part of their job duties.
Salary Context
This $110K-$150K 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 Snail Games USA, 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880. This role's midpoint ($130K) sits 28% below the category median. Disclosed range: $110K to $150K.
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.
Snail Games USA AI Hiring
Snail Games USA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Beverly Hills, CA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 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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