Interested in this Research Scientist role at Bitus Labs LLC?
Apply Now →Skills & Technologies
About This Role
About the Role
We develop predictive and generative models of player behavior and the game systems that act on them, while also researching and optimizing the core mathematical models behind our games— particularly slot machines. In this role you will translate ambiguous product questions into well\-posed modeling problems and deliver solutions that are engaging, profitable, compliant with global regulatory standards, and reliable under production traffic and constraints. You will collaborate with game designers, product managers, and engineers in a fast\-paced, on\-site, consensus\-driven environment, driving both research and product impact.
We're looking for a researcher in Mathematics, Statistics, Computer Science, or a related quantitative field, with strength in probability, statistical/ML modeling, and game mathematics. Fluency in Chinese is required, and you must be able to work on\-site in Irvine, CA.
Responsibilities
Player Behavior \& Game\-System Modeling
- Develop models of player behavior—engagement, monetization, retention, anomaly detection— and the systems that act on them.
- Translate loosely\-specified product questions into modeling problems and select appropriate methods across probabilistic and generative models, gradient boosting, reinforcement learning and bandits, and constrained optimization.
- Apply player lifetime value estimation and retention analysis to inform game features and design decisions.
Game Mathematics Design \& Optimization
- Develop and refine core math models for slot machines and other games, meeting target RTP, hit frequency, and volatility goals.
- Design and verify random mechanics, bonus rounds, free spins, and jackpots through combinatorial analysis and detailed probability calculations.
- Balance player engagement, regulatory requirements, and long\-term profitability through innovative mathematical solutions.
Data, Simulation \& Evaluation
- Work with large, imbalanced, real\-world datasets; diagnose data quality, distribution shift, and leakage, and design evaluation that reflects true model performance.
- Perform large\-scale simulations and data analyses (Python, MATLAB, R, or custom code) to forecast game performance, validate math models, and optimize retention.
- Develop suitable quantitative indices to describe game performance accurately.
Production \& ML Workflow
- Take models to production with clean, reproducible code; collaborate on deployment, monitoring, and inference constraints.
- Build and operate ML pipelines on cloud infrastructure (AWS), including training, deployment, and monitoring of production models.
Regulatory Compliance \& Certification Support
- Prepare math models, simulation reports, and documentation for certification by global regulatory bodies (e.g., GLI, BMM) across North America, Europe, and Asia.
Cross\-Functional Collaboration \& Continuous Improvement
- Translate complex math/ML concepts into compelling gameplay and clear player communication for both technical and non\-technical audiences.
- Play\-test in\-development games, analyze live performance data, and maintain design documentation (par sheets, pay tables, control files).
Required Qualifications
- PhD in Mathematics, Computer Science, Statistics, or a related quantitative field (new graduates welcome), OR an MS with 2–3 years of industry experience as an Applied Scientist or ML Engineer.
- Strong expertise in combinatorics, probability theory, and statistics, with demonstrable skill in mathematical modeling for game design.
- Strong data intuition: ability to identify distribution shift and leakage and to select metrics appropriate to the problem.
- Solid software engineering fundamentals—version control, testing, reproducibility—and sound judgment on when a prototype must become production software.
- Demonstrated ability to drive a modeling problem from formulation to a working solution independently.
- Experience with simulation/programming tools such as Python.
- Fluency in Chinese (Required) and the ability to communicate complex concepts to technical and non\-technical audiences.
- Must be able to work on\-site in Irvine, CA 92618\.
Preferred / Nice to Have
- Familiarity with AWS and the end\-to\-end ML workflow, from modeling through deployment, monitoring, and inference constraints.
- Depth in reinforcement learning and bandits, probabilistic/generative modeling, or constrained/convex optimization and linear programming.
- Experience with agentic AI systems or generative models (e.g., LLMs, diffusion models).
- Background in gaming, simulation, recommender systems, or fraud/anomaly detection.
- Experience designing simulation or synthetic\-data pipelines.
- Research experience with publications; experience with both land\-based and online games.
- Familiarity with programming tools C\+\+.
- Knowledge of player behavior modeling and player lifetime value estimation.
- Passion for gaming and a creative yet analytical approach to game design.
Pay: From $100,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Parental leave
- Vision insurance
Language:
- Chinese (Required)
Ability to Commute:
- Irvine, CA 92618 (Required)
Ability to Relocate:
- Irvine, CA 92618: Relocate before starting work (Required)
Work Location: In person
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 4,133 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Bitus Labs LLC, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $223,400 based on 307 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Bitus Labs LLC AI Hiring
Bitus Labs LLC has 3 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer. Based in Irvine, CA, US.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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.