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About This Role
Application Notice:
We encourage you to apply thoughtfully by selecting one position that best matches your qualifications and interests. You may submit up to two active applications at a time. Please consider your location choice carefully—we recommend applying where you envision building your future.
The Firm:
Unlock the Boundless Horizons of Tax, Valuation, and Business Expertise with Andersen! At Andersen, we don't just offer a career; we provide a thrilling expedition into the world of Tax, Valuation, and Business Advisory. We stand as a trailblazing force with the most extensive global presence among professional services organizations. You'll embark on a journey that transcends the ordinary, working with extraordinary clients spanning every industry, regardless of their size, because at Andersen, we are free from independence\-related constraints that may hinder other firms.
But that's not all; we're more than just a company; we're a community that thrives on diversity, inclusivity, and collaboration. Our focus is on your development helping you flourish as leaders, colleagues, and trusted advisors. We equip you with world\-class education, immersive experiences, and invaluable mentorship to support your rise to the top.
We believe in your potential and invest in it to build a legacy that extends beyond your wildest dreams. Bring your ambition, your enterprising spirit, and your burning desire to be the best. Your future mirrors the limitless possibilities of our future. Join us at Andersen, and together, let's write the story of your success!
The Role:
We are looking for a Senior Manager, AI \& Data Science Engineer to join our internal National Tax Technology group, a team dedicated to building the analytical tools, data infrastructure, and AI\-powered applications that make our tax practice smarter and faster. You will work at the intersection of machine learning, data engineering, and generative AI — translating complex tax workflows into scalable technology solutions used daily by thousands of tax professionals.
This is a high\-impact, full\-stack data science role. You will own end\-to\-end development: from standing up pipelines and wrangling structured and unstructured tax data to deploying models and LLM\-powered applications in production. You will collaborate closely with tax subject matter experts, IT, and product stakeholders to understand problems deeply and build solutions that actually get used. What You'll Do Machine Learning \& AI* Design, train, and evaluate ML/AI models and workflows for tax use cases such as risk classification, anomaly detection, and predictive compliance analytics.
- Build and maintain model pipelines from feature engineering through deployment, monitoring, and retraining.
- Work with tax domain experts to translate regulatory and process knowledge into model features and evaluation criteria.
Generative AI \& LLM Applications* Develop LLM\-powered tools for tax research, document review, and summarization using retrieval\-augmented generation (RAG) and prompt engineering techniques.
- Evaluate and fine\-tune foundation models for domain\-specific tax and regulatory language.
- Build safeguards and evaluation frameworks to ensure accuracy and auditability in AI\-generated outputs — a non\-negotiable in a tax context.
Data Engineering* Design and maintain data pipelines that ingest, transform, and serve structured tax data (ERP outputs, trial balances, return data) and unstructured data (regulatory documents, correspondence)
- Build and maintain data models that serve both ML/AI workloads and downstream analytics.
- Ensure data governance, lineage, and quality standards are met.
- Design and deploy systems engineering and data integration architecture and tools (APIs, etc.)
Analytics \& Insights* Build dashboards and analytical tools that give tax teams visibility into workflow performance, compliance metrics, and process efficiency.
- Conduct ad hoc quantitative analyses to support tax leadership decisions and technology investment prioritization.
The Requirements:
- 5\-7 years of experience in a data science, machine learning engineering, or data engineering role
- BA in Computer Science, Data Science or Statistics; Master’s degree preferred, not required
- Proficiency in Python (pandas, scikit\-learn, PyTorch or TensorFlow) and SQL
- Hands\-on experience deploying ML models to production (MLflow, SageMaker, Vertex AI, or equivalent)
- Experience building or integrating LLM\-based applications (Claude, PLTR, OpenAI API, MS Azure OpenAI, LangChain, or similar)
- Experience with cloud data platforms and pipeline orchestration (Airflow, dbt, or equivalent)
- Strong communication skills — you can explain a model's behavior to a tax partner who has never heard of gradient descent
Preferred* Exposure to tax, finance, or accounting data (ERP systems, ONESOURCE, Corptax, or similar platforms)
- Experience with vector databases and RAG architectures (Pinecone, Weaviate, pgvector)
- Familiarity with document processing pipelines (OCR, PDF extraction, entity recognition on financial documents)
- Knowledge of data governance and auditability requirements in regulated industries
Compensation and Benefits
Our firm offers competitive base compensation, benefits package, and a discretionary employee bonus program for eligible employees based on individual and firm performance metrics per the defined program guidelines. For individuals hired to work in Los Angeles, San Francisco and New York, the expected salary for this role is $200,000\. to $250,000\.; the actual salary offer can vary based upon employee qualifications.
Benefits: Employees (and their families) are covered by medical, dental, vision, and basic life insurance. Employees are able to enroll in our firm’s 401(k) plan upon hire. We offer paid time off, beginning at 200 hours annually and provide twelve paid holidays throughout the calendar year. For a full listing of benefit offerings, please visit https://www.andersen.com/careers/faqs.
Compensation: In addition to competitive base compensation, our firm offers annual discretionary bonuses based on firm and individual performance and other forms of discretionary compensation that would be offered to the hired applicant in addition to their established salary range scale.
Applicants must be currently authorized to work in the United States on a full\-time basis upon hire. Andersen will not consider candidates for this position who require sponsorship for employment visa status now or in the future (e.g., H\-1B status). *Andersen Tax welcomes and encourages workforce diversity. We are an equal opportunity employer. Applicants and employees are considered for positions and are evaluated without regard to race, color, national origin, ancestry, religion, sexual orientation (including gender identity and gender expression), mental disability, physical disability, sex/gender (including pregnancy, childbirth, and related medical conditions), age, marital status, military status, veteran status, genetic information, or any other characteristic protected by federal, state or local laws or regulations. All qualified individuals, including those with criminal histories, will be considered in a manner consistent with the requirements of applicable state and local laws. Additionally, we make every effort to provide reasonable accommodations to qualified individuals with disabilities.* ANDERSEN TAX LLC NOTICE FOR JOB APPLICANTS
Salary Context
This $200K-$250K range is above the 75th percentile 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 Andersen, 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 ($225K) sits 26% above the category median. Disclosed range: $200K to $250K.
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.
Andersen AI Hiring
Andersen has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $250K - $250K.
Location Context
AI roles in Los Angeles pay a median of $189,000 across 1,686 tracked positions. That's 6% below the national 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 3,824 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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