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About This Role
About the Role
We're an online gaming company using AI to power and personalize player experiences. This role sits within the AI Engineering team, which is responsible for taking AI capabilities into production. This role focuses primarily on agent systems, with model deployment and inference engineering as a secondary responsibility.
We do not train foundation models from scratch. Our focus is on production AI systems, model adaptation, inference optimization, and agentic applications.
Responsibilities
Agent Systems — Primary
- Design, build, and optimize LLM\-powered agents, including planning, tool use, workflow orchestration, and multi\-step reasoning
- Architect memory systems, including short\-term memory, long\-term memory, context management, and session state
- Build and optimize RAG pipelines for relevance, grounding, freshness, and retrieval quality
- Design and operate vector\-store infrastructure (e.g., pgvector, Milvus, Qdrant, Weaviate)
- Define evaluation methodologies for agents, prompts, and workflows
- Optimize end\-to\-end agent quality, latency, reliability, and operating cost
Model Deployment \& Inference — Secondary
- Build and operate production inference services that are low\-latency, high\-concurrency, and highly reliable
- Serve online\-learning models (e.g., contextual bandits and reinforcement learning policies) with real\-time inference and online parameter or weight updates
- Deploy and optimize AI inference systems for latency, throughput, reliability, and resource efficiency
- Analyze and resolve inference\-serving bottlenecks
- Support deployment and serving of recommendation, ranking, and reinforcement learning models developed by research scientists
- Apply lightweight model adaptation techniques (e.g., LoRA, QLoRA, PEFT) when appropriate for domain\-specific requirements
MLOps — Supporting Both
- Build and maintain deployment pipelines, observability systems, and tracing infrastructure for agents and serving endpoints
- Monitor quality regression, performance degradation, and model drift
- Maintain version control for models, prompts, datasets, and agent configurations
- Contribute to automated validation, testing, and CI/CD workflows for AI systems
Collaboration
- Partner with research scientists, backend engineers, and data scientists to integrate AI systems into production products
- Document systems, best practices, and internal tooling
- Contribute to engineering standards and operational excellence across AI initiatives
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field
- 3\+ years of industry experience in Machine Learning Engineering or related roles
- Strong software and systems engineering experience, including building low\-latency, reliable production services in languages such as Go, Rust, C\+\+, or equivalent
- Experience building or supporting real\-time inference systems for recommendation, ranking, contextual bandits, reinforcement learning, or similar adaptive machine learning applications Strong experience with PyTorch and the Hugging Face ecosystem
- Experience building production LLM or agent applications (e.g., LangGraph, LlamaIndex, or equivalent frameworks)
- Hands\-on experience with RAG systems, embeddings, and vector databases
- Experience evaluating and monitoring LLM or agent systems in production
- Experience deploying and optimizing production machine learning or LLM systems
- Understanding of inference runtime behavior, resource utilization, latency optimization, and production serving performance
- Experience with Docker and Kubernetes
- Experience with cloud platforms such as AWS, GCP, or Azure
- Fluent Mandarin Chinese
Preferred / Nice to Have
- Experience fine\-tuning open\-weight LLMs using LoRA, QLoRA, PEFT, or related approaches
- Familiarity with the underlying algorithms used in recommender systems, ranking systems, contextual bandits, or reinforcement learning
- Experience with custom GPU kernel development using CUDA or OpenAI Triton
- Experience with graph\-level optimization and low\-level inference performance tuning
- Experience with large\-scale distributed training (e.g., FSDP, DeepSpeed, multi\-GPU workloads)
- Experience deploying models to edge environments using TFLite, CoreML, or NPU accelerators
- Strong understanding of CI/CD principles and deployment workflows
- Background in gaming, gaming AI, or player personalization systems
- Experience with distributed systems, Spark, Hadoop, or large\-scale data infrastructure
Pay: From $130,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Parental leave
- Retirement plan
- 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
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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Bitus Labs LLC, 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 $185,000 based on 13,200 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 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 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).
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 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
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