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About This Role
Block builds simple, powerful tools that make progress towards an economy that’s truly open to all.
Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we’re helping build a financial system that is open to everyone. Join us.
The Role
The AI & DX (Developer Experience) Analytics team exists to measure and improve Block's developer experience and investment in AI. We turn raw engineering signals (agent session data, pull request and CI activity, and AI spend) into reporting and analysis that shape leadership's AI investment decisions, and drive improvement in AI ROI and developer experience. Your work will be the trusted source of truth that engineers and executives use to decide where AI investment is working and where to redirect it.
We're hiring a Senior Analytics Engineer to own parts of that pipeline end to end. This is a broad, high-visibility role with a lot of white space: you will instrument new telemetry at the source and build production pipelines that turn it into governed data, then turn that governed data into executive-facing reporting and the analysis that surfaces concrete opportunities to improve AI ROI and developer experience. You'll also help pioneer one of the most consequential open problems in the industry right now: measuring the productivity impact of an increasingly AI- and agent-augmented engineering workforce, where there is no textbook answer and real room to set the standard. You'll define new metrics and craft the visual narrative — the charts, dashboards, and presentation materials — that make them land with an executive audience. AI-first workflows are central to how this team operates and critical to succeeding in this role: using AI coding agents is a default part of how you build and maintain pipelines and dashboards. You'll excel in this role if you're as comfortable defining and visualizing a brand new executive-facing metric from a messy signal as you are debugging why a pipeline silently stalled overnight.
You Will
Instrumentation & Telemetry:
- Instrument and extend the raw telemetry captured about AI usage, code changes, CI/CD activity, and spend — building new data capture, not only transforming what already exists. This is core to the role, as evolving AI tools and increased AI adoption constantly creates new signals that need to be captured.
- Partner with data engineers on production ETL pipelines that land that telemetry into governed tables, with the freshness monitoring, backfills, and alerting that keep it trustworthy, rather than a one-off script.
- Define new metrics out of ambiguous or evolving signals, and earn stakeholder trust in a new number by validating and reconciling it until it holds up.
Dashboards & analysis:
- Build and ship the executive-facing dashboards and visualizations that turn governed metrics into decision-ready reporting — with the polish and precision a room of executives will scrutinize.
- Run the analysis that a new chart or metric needs and anticipate questions that an executive audience will ask, then bring senior stakeholders a recommendation, not just a number, and defend the methodology behind both.
- Translate complex, technical findings into a clear narrative: the headline chart and the one sentence that makes an executive act on it.
Across both:
- Use AI coding agents as a default part of how you write, test, and maintain data pipelines, dashboards, and analyses.
- Partner across data engineering, applied AI, and data science teams to keep telemetry connected end to end — the raw signal you capture directly feeds the session classifiers, not just the dashboards you build.
- Turn what the data shows into action: flag where AI spend isn't paying off or where developer friction is most costly, and push the tooling, process, or investment changes that address it.
- Push the industry-frontier work of quantifying AI and agent productivity impact — there's no established playbook, and this team sets one.
Qualifications
You are:
- Self-sufficient across the full analytics loop: instrumentation, pipeline building, metric definition, and dashboards — not someone who hands off past the query.
- At home in messy, evolving telemetry, with the judgment to know when a new number is right and when it needs a second look before an executive sees it.
- A strong product thinker: you get at what a stakeholder actually needs from a metric or chart, not just what they asked for.
- Comfortable with ambiguity on a genuinely unsolved problem — measuring AI/agent productivity impact — and motivated to go set the standard rather than wait for one.
- An exceptional communicator, able to defend a metric's methodology and a chart's framing directly to senior stakeholders.
- Thoughtful about using AI coding agents in your own craft: fast to build with them, and rigorous about evaluating what they produce rather than taking it on faith.
You have:
- 8+ years in analytics engineering, data engineering, or business intelligence, owning production data pipelines and insights end to end.
- Strong SQL and Python, with hands-on experience building and operating data pipelines against a cloud data warehouse such as Snowflake.
- Experience partnering with engineering teams to instrument new event or telemetry data, not only transforming what's already captured.
- Experience building dashboards or internal tools that non-technical stakeholders rely on to make decisions.
- Comfort with git, CI/CD systems, and debugging production pipeline failures such as retries and backfills.
Even better:
- Experience with a metrics-governance or semantic-layer system, such as a metrics store, dbt semantic layer, or LookML.
Technologies we use and teach:
- SQL (Snowflake), Python, TypeScript and React, Git, CI/CD pipelines, a governed metrics store, AI coding agents, and LLM-based classification tooling.
We’re working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.
We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible.
While there is no specific deadline to apply for this role, U.S. roles are typically open for an average of 55 days before being filled by a successful candidate. Please refer to the date listed at the top of this job page for when this role was first posted.
Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future.
Zone A: $163,600—$245,400 USD Zone B: $155,400—$233,200 USD Zone C: $146,900—$220,300 USD Zone D: $139,000—$208,600 USDApplication Guidelines
Candidates may submit up to 9 active applications within a 60-day period. Reapplications to the same role are accepted 90 days after a previous application has been reviewed.
Use of AI in Our Hiring Process
We may use automated AI tools to evaluate job applications for efficiency and consistency. These tools comply with local regulations, including bias audits, and we handle all personal data in accordance with state and local privacy laws.
Contact us here with hiring practice or data usage questions.
Every benefit we offer is designed with one goal: empowering you to do the best work of your career while building the life you want. Remote work, medical insurance, flexible time off, retirement savings plans, and modern family planning are just some of our offering.
Block, Inc. (NYSE: XYZ) builds technology to increase access to the global economy. Each of our brands unlocks different aspects of the economy for more people. Square makes commerce and financial services accessible to sellers. Cash App is the easy way to spend, send, and store money. Afterpay is transforming the way customers manage their spending over time. TIDAL is a music platform that empowers artists to thrive as entrepreneurs. Bitkey is a simple self-custody wallet built for bitcoin. Proto is a suite of bitcoin mining products and services. Together, we’re helping build a financial system that is open to everyone.
Salary Context
This $139K-$245K range is above the median 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 Block, 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($192K) sits 9% below the category median. Disclosed range: $139K to $245K.
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.
Block AI Hiring
Block has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco Bay Area, CA, US, New York, NY, New York, NY, US. Compensation range: $245K - $395K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 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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