Interested in this AI/ML Engineer role at Block?
Apply Now →About This Role
Block is building a new generation of AI-powered products with the potential to create entirely new ways for businesses to operate, engage with customers, and grow. We are looking for an experienced commercial leader to turn these emerging products into meaningful businesses.
The Director of AI Commercialization will own the path from product to pilot to commercial validation, and turn this into a repeatable go-to-market motion across Block’s emerging AI portfolio. This role will focus on enterprise customers and strategic partners.
This is a senior, highly entrepreneurial role, and requires someone who has done this before: taken technically complex, early-stage products into the market, secured sophisticated enterprise customers and partners, built and converted pilots, established pricing and commercial models, and ultimately created a sales and partnership motion that could scale.
Initially, this leader will be highly hands-on. They will personally develop our most important enterprise customers and partners, structure our earliest commercial engagements, and work alongside Product and Engineering to shape what we build. As we establish product-market fit, they will determine how we scale the motion and build the organization required to support it.
What You’ll Do
Own AI commercialization
- Develop and own the commercialization strategy for Block’s emerging AI products, including target markets, enterprise customer segments, partner types, use cases, positioning, pricing, packaging, and routes to market.
- Translate technically sophisticated AI capabilities into clear, differentiated value propositions for enterprise customers and partners.
- Assess market opportunities and determine where Block has the strongest right to win.
- Establish clear commercial milestones and determine when products are ready to move from experimentation to broader distribution.
- Operate as the senior commercial counterpart to Product and Engineering leaders building Block’s AI portfolio.
- Bring together cross-functional infrastructure required to support this motion at scale.
Win lighthouse enterprise customers and strategic partners
- Personally source, develop, and close relationships with sophisticated enterprise customers and strategic partners.
- Engage credibly with C-suite executives and senior technology, product, operations, business development, and commercial leaders.
- Identify where strategic partnerships can accelerate distribution, unlock new use cases, or create differentiated product capabilities.
- Sell products that may still be evolving, creating conviction around the vision while setting appropriate expectations about what exists today.
- Build a portfolio of lighthouse customers and partners that provides product validation, commercial proof points, references, distribution, and a foundation for broader adoption.
Build pilots that become scaled commercial relationships
- Design commercial pilots with enterprise customers and partners with clear objectives, success metrics, economics, timelines, and paths to production and expansion.
Own the progression from initial conversation pilot production deployment or partnership launch* scaled commercial relationship.
- Work directly with customers, partners, Product, and Engineering throughout early implementations.
- Distinguish between strategically important market feedback and bespoke requests that should not drive the roadmap.
- Use early engagements to rapidly test product-market fit, willingness to pay, partnership models, implementation requirements, and the durability of demand.
Create the repeatable GTM and partnership model
- Turn what we learn from early customers and partners into a scalable commercial playbook.
- Establish segmentation, qualification, pricing, contracting, sales process, partner models, implementation, customer success, and expansion motions.
- Determine when products should be sold directly, through strategic partners, through existing Block Sales and Partnerships teams, or through a combination of channels.
- Identify opportunities to leverage Block’s existing enterprise relationships and broader ecosystem.
- Build the pipeline management, forecasting, metrics, and operating cadence required to manage the business.
Build the organization
- Start as a hands-on commercial leader rather than immediately building a large team.
- Determine what capabilities and roles are required as product-market fit develops.
- Recruit and lead a high-performing commercial organization when the opportunity is ready to scale.
- Create a thoughtful path for successful products to graduate from incubation into Block’s broader GTM organization where appropriate.
What We’re Looking For
- 15+ years of relevant commercial experience, including significant leadership responsibility in enterprise technology.
- This role requires a proven commercialization leader, not simply an experienced enterprise seller or partnerships executive.
- Direct, demonstrated experience taking new or emerging technology products from early market entry through commercial scale.
- A track record of personally securing lighthouse enterprise customers and strategic partners, structuring pilots and partnerships, and converting them into scaled commercial relationships.
- Deep experience selling complex technology to large enterprises and working with sophisticated strategic partners.
- Established credibility engaging C-suite, senior technical, product, business development, and commercial executives.
- Experience commercializing AI, data infrastructure, developer platforms, cloud infrastructure, or similarly complex and rapidly evolving technology.
- Strong understanding of how enterprises and strategic partners evaluate, pilot, procure, integrate, distribute, and scale emerging technologies.
- Demonstrated ability to develop pricing, packaging, partnership structures, commercial models, and routes to market where no established playbook exists.
- Strong product judgment and a history of working as a true partner to Product and Engineering.
- Experience building a new business inside of a technology company
- Experience building and leading high-performing commercial teams.
- The ability to operate at both the strategic and execution levels — from defining a market strategy to personally getting a difficult enterprise deal or strategic partnership across the line.
- Comfort making decisions with incomplete information and adjusting quickly as the product and market evolve.
Pay Transparency:
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. To find a location's zone designation, please refer to this resource. If a location of interest is not listed, please speak with a recruiter for additional information.
- All Zones: ($239,600 - $359,400)
Application 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 $239K-$359K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 in Demand for This Role
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 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($299K) sits 41% above the category median. Disclosed range: $239K to $359K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Block AI Hiring
Block has 4 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 San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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