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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
As a Staff Applied Machine Learning Engineer focused on Intelligent Data, Signals \& Systems, you will build production ML systems that transform customer behavior, product context, model outputs, and feedback loops into trusted signals used by recommendations, ranking, risk\-aware decisioning, growth, and customer intelligence systems.
This role centers on customer intelligence and reusable model\-derived signal systems: ranking and retrieval, recommendations, search, propensity and churn/LTV, next\-best\-action decisioning, experimentation, and feedback loops. These systems help product, growth, fraud, and risk teams make better decisions with clear freshness, provenance, confidence, and evaluation guarantees.
The work combines production ML systems with composable signal interfaces that can be consumed by product surfaces, decision engines, internal tools, and verified AI\-assisted workflows. The role is flexible across Applied ML Engineering domains while still requiring deep expertise.
#### You Will
- Build and operate production ML systems that turn customer and product context into trusted signals, rankings, recommendations, and decision capabilities.
- Design production data and signal contracts that define intended use, freshness, provenance, confidence, eligibility, and calibration for downstream consumers.
- Own ranking, retrieval, recommendation, search, propensity, and next\-best\-action systems end to end, from feature and candidate generation through serving, experimentation, monitoring, and feedback loops.
- Evaluate customer and business impact beyond short\-term conversion, including trust, fairness, access, risk, compliance, long\-term engagement, and segment\-level performance.
- Partner across product, growth, data, platform, modeling, risk, and compliance to translate ambiguous goals into measurable ML system designs.
- Use AI and agents to accelerate development, analysis, testing, documentation, and operations while exposing reusable capabilities to product services, internal tools, and AI\-assisted workflows.
#### You Have
- 12\+ years building and operating production software and ML systems for business\-critical products.
- Deep expertise in intelligent systems such as ranking/retrieval, recommendations, search, personalization, growth and lifecycle ML, customer intelligence, propensity/churn/LTV, next\-best\-action, or model\-derived risk signals.
- Strong production ML judgment across feature pipelines, model serving, experimentation, monitoring, feedback loops, online/offline consistency, and reliable signal interfaces.
- Ability to evaluate impact beyond short\-term conversion, including trust, fairness, access, risk, compliance, and long\-term engagement.
- Experience using AI\-assisted engineering tools with appropriate verification, testing, and review for customer\-impacting systems.
#### Nice to Have
- Experience with semantic retrieval, embeddings, two\-tower models, graph features, LLM\-powered retrieval or decision systems, entity resolution, or real\-time personalization.
- Experience with experimentation, online evaluation, interleaving, counterfactual evaluation, multi\-objective optimization, or long\-term holdouts.
- Experience building reusable feature/signal platforms, decision services, customer intelligence layers, model\-derived data products, or agent\-assisted operations.
#### Technologies We Use and Teach
We do not expect candidates to have used our exact stack. We do expect strong production engineering fundamentals, deep domain expertise in intelligent ML systems, and judgment about how ML\-derived signals should be used safely in customer\-impacting products. Examples of technologies and methods include:
- Python, Java, Kotlin, SQL.
- TensorFlow, PyTorch, XGBoost/LightGBM, ranking/retrieval systems, embeddings, semantic search, recommendation frameworks.
- Event streams, batch pipelines, feature stores, model\-serving infrastructure, workflow orchestration, experimentation systems, and data warehouses/lakehouses.
- Cloud infrastructure, Kubernetes, observability tooling, coding agents, evaluation harnesses, and agent\-assisted operations 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:
$276,800—$415,200 USD
Zone B:
$276,800—$415,200 USD
Zone C:
$276,800—$415,200 USD
Zone D:
$276,800—$415,200 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 $276K-$415K 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 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 $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 ($346K) sits 93% above the category median. Disclosed range: $276K to $415K.
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.
Block AI Hiring
Block has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span San Francisco Bay Area, CA, US, Seattle, WA, US, New York, NY, US. Compensation range: $225K - $415K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% 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 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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