Interested in this AI/ML Engineer role at Anrok?
Apply Now →Skills & Technologies
About This Role
Anrok is the leading tax automation platform enabling businesses to expand globally without compliance complexity.
As the digital economy has grown 6x over the last decade, software businesses have gone from not worrying about sales tax to needing to monitor exposure, calculate rates, and file returns across 50 US jurisdictions and 100\+ countries. This creates a critical bottleneck for companies that should be able to transact with customers everywhere.
Anrok eliminates this complexity by connecting with billing and payment systems to automate tax monitoring, calculations, and filing end\-to\-end. Our unified platform handles the ever\-changing maze of tax laws at municipal, state, and federal levels—so companies can focus on growth, not compliance.
Our customers include:
- 40% of Forbes Top 50 AI companies
- 20% of Forbes Top 100 Cloud companies
- Top companies like Notion, Anthropic, and Cursor
We're making compliant digital commerce a reality for companies big and small, backed by over $100M from leading investors including Sequoia, Spark, Index, and Khosla Ventures.
As the Platform and AI Infrastructure Engineering Manager, you'll own the shared systems and AI infrastructure on which the rest of Anrok builds on. As we grow, well\-designed platform spanning authn and authz, RBAC, FGAC, audit trails and more, along with AI infrastructure is the backbone that lets every other team move fast without compromising the compliance workflows our customers depend on.
You have the opportunity to drive execution and strategy for an exceptional team, help shape the product roadmap, and coach and develop senior engineers. We work primarily in TypeScript and React, but prior experience is not required.
In this role, you will
--------------------------
- Hire, manage, and grow a talented team building the platform and AI infrastructure that powers Anrok's product.
- Set the product and technical strategy, while driving processes for high quality execution.
- Write and review code alongside the engineers on the team as needed.
- Foster a collaborative and healthy culture of feedback, opportunity, and growth for the team.
- Contribute to the Anrok engineering culture and initiatives.
- Find appropriate uses of AI in both the creation of automations or as the automation itself.
- Help grow the team—mentoring engineers, contributing to technical culture, and raising the bar on how we build.
What excites us
-------------------
- 3\+ years of experience managing engineering teams, with significant experience as a software engineer.
- Track record of figuring out how to set individuals and teams up for success.
- You enjoy coding and have strong technical acumen to guide the team through product and technical decisions.
- Experience building AI infrastructure—you've built and operated the systems beneath LLM\-powered or agentic products in production, and you understand firsthand what makes them reliable and maintainable at scale.
- A track record of building shared services and components that support internal engineering teams—the foundational primitives and tooling that the rest of the org builds on, designed to make every other team faster.
- You’re curious and motivated to learn new tools — you've experimented with AI in your work or on your own, you're excited about what's possible, and you've built something with it (an application, a workflow, a creative solution to a real problem) that you can walk us through.
What we offer
-----------------
- The equity upside of an early\-stage startup with the product\-market fit of a later\-stage company.
- Daily lunch and snacks for those working out of our office hubs.
- Medical, dental, and vision insurance covered 100%.
- One Medical membership covered, flexible sick benefits, and more.
- Annual learning and development stipend for books, online courses, and conferences, as well as a curious team to share your learnings with.
- Annual team off\-sites and in\-person opportunities around our growing Anrok hubs.
- Home office setup stipend to ensure you have the equipment you need to thrive at work.
At Anrok, we embrace a dynamic and flexible hybrid work environment based out of our growing office hubs \- San Francisco, New York City, and Salt Lake City where we collaborate in\-person 3 days per week.
*Please be aware, job\-seekers may be at risk of targeting by malicious actors looking for personal data. Anrok recruiters will only reach out via LinkedIn or email with an* *anrok.com* *domain. Any outreach claiming to be from Anrok via other sources should be ignored.*
Compensation Range: $200K \- $280K
Salary Context
This $200K-$280K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Anrok, 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. This role's midpoint ($240K) sits 30% above the category median. Disclosed range: $200K to $280K.
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.
Anrok AI Hiring
Anrok has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span New York, NY, US, San Francisco, CA, US. Compensation range: $280K - $280K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.