Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams - People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more - provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block. The Role The Credit and Lending team is responsible for the predictive intelligence that underpins Block's primary capital-intensive products. These products unlock unique access to credit for our customers, many of whom are otherwise underbanked and underserved by the traditional financial system. As a Machine Learning Engineer within Square Financial Services (SFS), you will occupy a high-leverage role at the intersection of regulated banking and advanced autonomous systems. This position requires full-stack ownership of the credit engine, from the curation of novel data signals to the implementation of the decisioning logic that drives Block's top-line growth. Our credit products are material drivers of the company's profitability and are frequently highlighted in executive reviews and quarterly earnings reports. We are seeking a scientifically-minded contributor capable of delivering extraordinary individual leverage to expand our underwriting capabilities into previously untapped segments through pragmatic policy evolution and advanced modeling techniques. You Will - Apply a rigorous scientific mindset to the challenge of underwriting new customer segments, involving the evaluation of alternative external data sources and the deployment of advanced architectures to enhance predictive accuracy. - Lead complex ML Operations and Infrastructure initiatives that advance our modeling capabilities, such as scaling data ingestion or enabling the use of more complex neural networks. - Design and implement the full credit modeling stack, taking responsibility for the entire lifecycle of credit decisioning and ensuring models are robustly integrated into production environments. - Use data science techniques to leverage new data sources for modeling, making sense of messy datasets and bringing clarity to business decisions. - Identify and execute material improvements to credit policy, applying an analytical lens to determine where technical or logic shifts can yield significant positive outcomes for the customer and the bank's portfolio. - Support team members in ad-hoc and scheduled updates to existing models, and help troubleshoot issues in a real-time production environment. - Operate effectively within the framework of a regulated bank (SFS), balancing rapid innovation with the requirements of safety, soundness, and compliance. You Have - Minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years experience, with a focus on developing and deploying machine learning and statistical models in production environments. - A degree in a technical field (e.g., Computer Science, Mathematics, Statistics, Physics, or Engineering). We have a strong preference for candidates with a demonstrated track record of scientific research or an advanced degree. - Strong quantitative intuition and data visualization skills, with a proven ability to conduct sophisticated ad-hoc and exploratory analysis. - Full-stack proficiency preferred, including the ability to contribute across the entire technical stack-from data pipelines to production-grade software architecture. - The versatility to communicate clearly with both technical and non-technical audiences, particularly in the context of high-visibility projects and executive stakeholders. - A pragmatic approach to problem-solving, with a willingness to utilize whichever tool is most appropriate for the situation while balancing complex business, technical, and regulatory constraints. - Experience with tree-based models and gradient boosting is helpful but not required; we value the ability to adapt and learn new methodologies as the credit landscape evolves. 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. Want to learn more about what we're doing to build a workplace that is fair and square? Check out our I+D page . 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. Zone A: $228,700 - $343,100 USD Zone B: $217,300 - $325,900 USD Zone C: $205,900 - $308,900 USD Zone D: $194,500 - $291,700 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. Check out our other benefits at Block. 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.

Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams - People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more - provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block. The Role The Credit and Lending team is responsible for the predictive intelligence that underpins Block's primary capital-intensive products. These products unlock unique access to credit for our customers, many of whom are otherwise underbanked and underserved by the traditional financial system. As a Machine Learning Engineer within Square Financial Services (SFS), you will occupy a high-leverage role at the intersection of regulated banking and advanced autonomous systems. This position requires full-stack ownership of the credit engine, from the curation of novel data signals to the implementation of the decisioning logic that drives Block's top-line growth. Our credit products are material drivers of the company's profitability and are frequently highlighted in executive reviews and quarterly earnings reports. We are seeking a scientifically-minded contributor capable of delivering extraordinary individual leverage to expand our underwriting capabilities into previously untapped segments through pragmatic policy evolution and advanced modeling techniques. You Will - Apply a rigorous scientific mindset to the challenge of underwriting new customer segments, involving the evaluation of alternative external data sources and the deployment of advanced architectures to enhance predictive accuracy. - Lead complex ML Operations and Infrastructure initiatives that advance our modeling capabilities, such as scaling data ingestion or enabling the use of more complex neural networks. - Design and implement the full credit modeling stack, taking responsibility for the entire lifecycle of credit decisioning and ensuring models are robustly integrated into production environments. - Use data science techniques to leverage new data sources for modeling, making sense of messy datasets and bringing clarity to business decisions. - Identify and execute material improvements to credit policy, applying an analytical lens to determine where technical or logic shifts can yield significant positive outcomes for the customer and the bank's portfolio. - Support team members in ad-hoc and scheduled updates to existing models, and help troubleshoot issues in a real-time production environment. - Operate effectively within the framework of a regulated bank (SFS), balancing rapid innovation with the requirements of safety, soundness, and compliance. You Have - Minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years experience, with a focus on developing and deploying machine learning and statistical models in production environments. - A degree in a technical field (e.g., Computer Science, Mathematics, Statistics, Physics, or Engineering). We have a strong preference for candidates with a demonstrated track record of scientific research or an advanced degree. - Strong quantitative intuition and data visualization skills, with a proven ability to conduct sophisticated ad-hoc and exploratory analysis. - Full-stack proficiency preferred, including the ability to contribute across the entire technical stack-from data pipelines to production-grade software architecture. - The versatility to communicate clearly with both technical and non-technical audiences, particularly in the context of high-visibility projects and executive stakeholders. - A pragmatic approach to problem-solving, with a willingness to utilize whichever tool is most appropriate for the situation while balancing complex business, technical, and regulatory constraints. - Experience with tree-based models and gradient boosting is helpful but not required; we value the ability to adapt and learn new methodologies as the credit landscape evolves. 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. Want to learn more about what we're doing to build a workplace that is fair and square? Check out our I+D page . 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. Zone A: $228,700 - $343,100 USD Zone B: $217,300 - $325,900 USD Zone C: $205,900 - $308,900 USD Zone D: $194,500 - $291,700 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. Check out our other benefits at Block. 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.

Role Description We’re hiring a Machine Learning Engineer to own inference for Rime’s models in production. Voice is unforgiving because every millisecond shows up in the conversation. You’ll build the systems that turn our models into the lowest-latency, highest-throughput, most reliable speech systems in the industry. What You’ll Own - In-house real-time speech-first inference stack: model compilation, kernel optimization, batching strategy, streaming output, the path from checkpoint to first-audio-byte. - Latency systems: TTFB targets across regions, KV cache management, speculative decoding, scheduler design. - Deployment flexibility: cloud, on-prem, BYOC (SageMaker, Connect), the packaging and runtime story across heterogeneous environments. - Inference for full- and half-duplex models, including streaming codec encoding and decoding. Qualifications - Strong software engineering fundamentals: Rust, Python, C++/CUDA welcome, distributed systems, comfort across the stack. - Hands-on experience serving ML models at scale in production, ideally for low-latency or streaming workloads. - Deep familiarity with inference engines (vLLM, SGLang), SDKs (TensorRT, ONNX, CUDA Graphs, Triton), etc. - Working knowledge of speech synthesis and/or speech recognition techniques. - Familiarity with multiple speech representations (neural codecs, semantic tokens, mel/STFT) and how they shape inference cost. - Experience optimizing transformer or autoregressive model inference: KV caching, quantization, paged attention, speculative decoding. - Willing to roll up your sleeves on unglamorous performance work — flame graphs, NSight traces, kernel tuning, paired with the agency to build the abstractions so the team doesn’t stay stuck doing it by hand. - Bias toward shipping. Requirements - CUDA kernel authoring or Triton experience (nice to have). - GPU profiling and microarchitecture intuition (H100, A100, L40S, Blackwell) (nice to have). - Experience with parallel model training infrastructure (nice to have). - Multi-tenant inference scheduling and fairness (nice to have). - Comfort working close to research teams and influencing model architecture choices for inference-friendliness (nice to have). Benefits - Build the inference stack behind a category-defining voice AI company. - Direct collaboration with founders, including a CEO with a Stanford computational linguistics PhD who takes latency as seriously as you do. - The systems you build determine what experiences our customers can deploy. - Meaningful equity upside. - High ownership, high standards, low bureaucracy. What We Offer - Competitive base + meaningful early-stage equity. - Remote-friendly. - Visa sponsorship available. - Access to a proprietary, full-duplex, studio-quality conversational speech corpus. - Compute and tooling to do the work. - Direct influence on the future of voice AI. Company Description Rime builds voice AI for enterprises running customer experiences at scale. Our text-to-speech models are purpose-built for high-volume conversational deployments, engineered for the pronunciation accuracy, latency, and deployment flexibility that production environments actually demand. We started from a different premise than the rest of the field: voice AI isn’t bottlenecked by model architecture. It’s bottlenecked by data. So before we trained a single model, we built our own corpus: full-duplex, studio-quality conversational speech, recorded and annotated by PhD linguists. That’s our moat. It’s also why enterprises pick Rime when pilots need to convert into production. We’re backed by top-tier investors including Unusual Ventures, and we’ve built a team at the intersection of product, research, and craft. Building voice models is an art. We intend to master it.

• Design, develop, and deploy AI agents and machine learning models to solve complex HR challenges, such as employee sentiment analysis, talent acquisition optimization, and workforce planning. • Partner with HR stakeholders and data analysts to identify business needs and translate them into technical requirements for AI solutions. • Conduct exploratory data analysis to identify trends, patterns, and insights that can inform the development of AI models. • Monitor, evaluate, and fine-tune deployed AI models to ensure optimal performance and accuracy. • Stay up-to-date with the latest advancements in AI, machine learning, and data engineering to continuously improve our capabilities. • Design, build, and maintain robust data pipelines to ingest, transform, and store HR data from various sources (e.g., APIs, databases, flat files). • Optimize data architecture and databases to improve performance and scalability. • Collaborate with IT and other data professionals to ensure the seamless integration of data solutions and to maintain a scalable data infrastructure. • Adhere to data governance and security best practices to protect sensitive HR information.