Senior Level Sr Staff Machine Learning Engineer Jobs
Senior level sr staff machine learning engineer jobs place experienced engineers at the helm of model architecture decisions, production system ownership, and the cross-functional teams driving ML outcomes at scale. Openings cover 61% remote and hybrid settings across Technology & Software, Electronics & Hardware, and Automotive, with employers like Apple, General Motors, and SentiLink hiring at this level now.
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INTRODUCTION
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 USD
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.
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Who's Hiring



Top Industries Hiring
- Technology & Software46
- Electronics & Hardware19
- Automotive13
- Banking & Financial Services11
- Insurance4
Senior Level Sr Staff Machine Learning Engineer Jobs: Frequently Asked Questions
How do I get a senior level sr staff machine learning engineer job?
Employers at this level look beyond technical proficiency to evidence of independent ownership: leading end-to-end ML systems from research through production, setting modeling standards for a team, and influencing roadmap decisions. Candidates who stand out can articulate the business impact of past projects, demonstrate mentoring experience, and show a history of driving architectural choices rather than implementing someone else's design.
Which companies hire senior level sr staff machine learning engineers?
Companies hiring senior level sr staff machine learning engineers right now include Apple, General Motors, and SentiLink, based on current listings on Migrate Mate as of September 2026. Hiring at this level tends to concentrate in organizations with mature ML infrastructure, including large technology companies, AI-focused product firms, and enterprises running production-scale model pipelines.
Are there remote senior level sr staff machine learning engineer jobs?
Yes, remote and hybrid options are well represented at this level. About 61% of senior level sr staff machine learning engineer openings are remote or hybrid as of September 2026, reflecting the distributed team structures common at companies operating large ML platforms. On-site roles still exist, particularly where access to proprietary hardware or data infrastructure is required.
What makes a sr staff machine learning engineer role senior level?
Senior level sr staff machine learning engineer roles are defined by scope of ownership and organizational influence. Engineers at this level own entire ML systems rather than individual components, set technical direction for modeling and infrastructure, and are expected to mentor mid-level engineers. They operate with minimal supervision, drive cross-team alignment on ML strategy, and are accountable for production reliability and model performance outcomes at scale.
Which industries hire the most senior level sr staff machine learning engineers?
Senior level sr staff machine learning engineer roles concentrate in Technology & Software, Electronics & Hardware, and Automotive, based on current listings on Migrate Mate as of September 2026. These sectors tend to drive the highest demand because they operate data-intensive products or services where advanced modeling directly affects revenue, risk management, or customer experience at scale.