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About This Role
We are hiring a Mandarin-speaking Immigration Case Manager — AI Operations who combines strong U.S. immigration case-management experience with advanced AI and legal-technology skills.
This is not a traditional paralegal position.
We are building an AI-native immigration practice where highly capable case managers use AI and modern case-management technology to independently manage substantially more of the case lifecycle—from intake and evidence collection through drafting, quality control, and attorney-ready review.
You do not need to be an AI engineer or programmer.
You do need to be highly fluent with AI and modern business software and already know how to use Claude effectively for sophisticated immigration work.
We are specifically looking for someone who understands how to use Claude beyond basic prompting—for example, working with large case files and long-context documents, structuring complex immigration analysis, extracting and organizing evidence, identifying inconsistencies and missing information, developing evidence matrices, preparing and improving drafts, analyzing RFEs, performing quality control, and building repeatable AI-assisted workflows.
You should know how to give AI the right context and instructions, evaluate its output critically, verify it against source documents, identify hallucinations or unsupported conclusions, and iterate until the work product meets a professional standard.
AI proficiency is a core qualification for this position, not something we expect to teach from the beginning.
What You’ll Do
You will take ownership of immigration cases from assignment through attorney review, including:
- Communicating directly with clients in Mandarin and English
- Reviewing intake information, CVs, immigration records, and supporting documents
- Identifying missing information and proactively following up
- Managing case progress, deadlines, and outstanding items
- Preparing immigration forms and supporting documentation
- Organizing evidence and exhibits
- Assisting with petitions, recommendation letters, RFEs, and other case materials
- Using Claude extensively for document analysis, evidence analysis, drafting, case organization, and quality control
- Using modern case-management and business software efficiently
- Checking AI-generated work against the underlying record
- Identifying factual inconsistencies and potential issues
- Escalating matters requiring attorney judgment
- Developing better prompts, templates, checklists, and repeatable workflows
- Delivering organized, substantially complete cases for attorney review
The objective is straightforward:
Once a case is assigned to you, you should be capable of moving it from intake to attorney-ready review with minimal attorney intervention.
Who We’re Looking For
You may be a strong fit if you have:
- Professional fluency in Mandarin Chinese and English
- Meaningful U.S. immigration case-management or paralegal experience
- Advanced proficiency using Claude for professional immigration work
- Strong general AI literacy and the ability to work effectively with large amounts of information
- High proficiency with modern corporate software, document-management systems, cloud collaboration tools, and case-management platforms
- Experience independently managing immigration cases
- Excellent organization and attention to factual detail
- Strong written communication skills
- The ability to manage multiple matters without constant attorney supervision
- The judgment to distinguish routine production issues from matters requiring attorney attention
Experience with employment-based immigration—including EB-1A, EB-2 NIW, O-1, H-1B, L-1, PERM, I-140, and I-485—is highly valued.
Experience at a sophisticated business immigration practice is a plus, but not required.
What “AI Proficiency” Means to Us
We are not looking for someone whose AI experience consists primarily of asking ChatGPT or Claude to rewrite emails.
A strong candidate should be comfortable demonstrating how they use Claude to work through an actual immigration matter.
For example, you should be able to take a substantial client file and use AI intelligently to:
- understand and structure the case;
- extract relevant facts from multiple documents;
- identify missing or contradictory information;
- create timelines;
- analyze and categorize evidence;
- build evidence matrices;
- analyze publications and citation records;
- organize EB-1A/O-1 evidence by criterion;
- organize NIW evidence around the proposed endeavor;
- analyze an RFE against the existing record;
- prepare customized evidence requests;
- produce first drafts from verified source materials;
- compare drafts against underlying evidence;
- perform consistency and quality-control reviews;
- and turn successful approaches into reusable workflows.
You should also understand AI’s limitations.
Fast AI-generated work that contains unsupported facts, missed evidence, or hallucinations is not good work.
The person we want knows how to make AI powerful and reliable.
Why This Role Is Different
Most immigration case-management positions are still structured around traditional production methods.
This one is not.
We want to see what happens when an experienced immigration professional becomes exceptionally effective at combining:
Immigration knowledge + case ownership + Claude + modern software + operational judgment.
The objective is not simply to draft faster. It is to enable one excellent case manager to perform work that traditionally required substantially more administrative and junior-production support.
Career Growth
This position has a significant growth path for someone who performs exceptionally well:
Immigration Case Manager — AI Operations → Senior Immigration Case Manager — AI Operations → AI Operations / Case Management Lead → Manager of Case Management & AI Operations → Director of Case Management & AI Operations
As the firm grows, high performers may take responsibility for training other case managers, developing AI workflows and SOPs, overseeing quality control, managing teams, and helping design the firm’s broader AI-enabled immigration operating system.
This is particularly suited to someone who does not want to spend the next five years simply processing more cases in the same way.
We want someone who wants to become an expert in how immigration work itself is changing.
Pay: $50,000.00 - $85,000.00 per year
Benefits:
- Dental insurance
- Health insurance
- Vision insurance
Work Location: Remote
Salary Context
This $50K-$85K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At The Law Office of Zhang, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($67K) sits 68% below the category median. Disclosed range: $50K to $85K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
The Law Office of Zhang AI Hiring
The Law Office of Zhang has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $85K - $85K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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