The Role As a Staff Machine Learning Engineer in Fraud and Abuse ML Engineering, you will play a central role in designing, building, and evolving the machine learning systems that safeguard Block's ecosystem from fraud, abuse, and other malicious activity. You will architect and lead the development of high-scale, real-time ML systems that power our fraud decisioning across the Block network, protecting millions of customers and merchants while maintaining seamless user experiences. You Will Build and maintain real-time and batch data pipelines/APIs to support ML model inference at scale. Design elegant ML pipelines and services, prototype new approaches, and productionize solutions at scale. Collaborate with product and engineering teams to define data models and schemas for consistent and structured data flow. Integrate and enrich diverse internal and third-party data sources to enhance our feature store and modeling capabilities. Ensure data quality and completeness through automated validation, monitoring, and alerting. Develop new triggers and event hooks that support enhanced risk evaluations and detections. Participate in SEV management by rapidly integrating new data, deploying new features, and implementing stopgap controls to mitigate risk. Apply ML and engineering best practices to shape how Block develops, tests, and maintains ML-platform solutions. Support the integration of risk decisions and scores into downstream systems, ensuring they are consumed and acted upon correctly. You Have 12+ years of experience in software development and demonstrated technical initiative and leadership on previous machine learning projects. A proven ability to shape how teams effectively adopt and evolve AI practices; driving adoption of AI-first workflows by coaching leaders, identifying scalable use cases, and embedding quality and accountability in team norms. Experience leading experimentation cycles and building a shared understanding of how AI drives business outcomes. Curiosity and a passion for Block's mission of economic empowerment. Experience helping your teammates grow through mentorship and providing constructive feedback. A desire to be hands-on and help invent the future of AI. The ability to work autonomously in the rapidly evolving world of ML. Technologies We Use and Teach Python, Java and Kotlin Tensorflow and PyTorch AWS, Databricks and Kubernetes MySQL, DynamoDB, Kafka, Spark 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 a proud equal opportunity employer. We work hard to evaluate all employees and job applicants consistently, based solely on the core competencies required of the role at hand, and without regard to any legally protected class. 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 an inclusive workplace? Check out our Inclusion & Diversity page Full-time employee benefits include the following: Healthcare coverage (Medical, Vision and Dental insurance) Health Savings Account and Flexible Spending Account Retirement Plans including company match Employee Stock Purchase Program Wellness programs, including access to mental health, 1:1 financial planners, and a monthly wellness allowance Paid parental and caregiving leave Paid time off (including 12 paid holidays) Paid sick leave (1 hour per 26 hours worked (max 80 hours per calendar year to the extent legally permissible) for non-exempt employees and covered by our Flexible Time Off policy for exempt employees) Learning and Development resources Paid Life insurance, AD&D, and disability benefits These benefits are further detailed in Block's policies. This role is also eligible to participate in Block's equity plan subject to the terms of the applicable plans and policies, and may be eligible for a sign-on bonus. Sales roles may be eligible to participate in a commission plan subject to the terms of the applicable plans and policies. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans. 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.

It all started with an idea at Block in 2013. Initially built to take the pain out of peer-to-peer payments, Cash App has gone from a simple product with a single purpose to a dynamic ecosystem, developing unique financial products, including Afterpay/Clearpay, to provide a better way to send, spend, invest, borrow and save to our 50+ million monthly active customers. We want to redefine the world's relationship with money to make it more relatable, instantly available, and universally accessible. Today, Cash App has thousands of employees working globally across office and remote locations, with a culture geared toward innovation, collaboration and impact. We've been a distributed team since day one, and many of our roles can be done remotely from the countries where Cash App operates. No matter the location, we tailor our experience to ensure our employees are creative, productive, and happy. The Role Block has provided over $200 billion in credit to customers globally. Afterpay and Cash App Borrow are our two largest products in this space, expanding access to credit for consumers who are often underserved by traditional financial systems. Machine learning is the core of how these products work. Our models decide who gets credit, how much, and under what terms. They underwrite customers across a wide range of credit profiles, including many with thin or no traditional credit history. The modeling challenges are real: maintaining calibration across diverse borrower populations, designing features that generalize as the portfolio grows, and balancing approval rates against loss performance at every decision point. This requires strong fundamentals, disciplined experimentation, and continuous evaluation in production. On the Credit Modeling team, you will be a senior individual contributor building and evolving the ML systems behind these products. You will work across the full modeling lifecycle: problem formulation, feature development, training, calibration, experimentation, deployment, monitoring, and iteration. You will operate across two distinct lending products with different borrower populations, repayment structures, and regulatory surfaces. We use agentic engineering and AI tooling to build reliable, high-velocity workflows that enable this work. That includes code generation, automated testing, documentation, and developer tooling. You will help define how these practices scale across the team in ways that are rigorous, auditable, and trusted. This is a team that values high output and rigor. We move fast, we test carefully, and we hold our work to a high standard because the models we build determine real credit outcomes for real people. This role is fully remote for candidates based in the US or Canada. You Will Build, evaluate, and maintain underwriting and decisioning models across Cash App Borrow and Afterpay. Design and evolve credit decision frameworks, including the modeling, automation, and policy logic that manage credit exposure over time. Design and run experiments to evaluate model performance, measure impact on approval rates and loss, and inform credit policy decisions. Develop deep understanding of borrower behavior, repayment dynamics, and portfolio structure across both products, and use that to inform model design and decision logic. Contribute analysis and perspective that inform portfolio-level decisions, including explaining model behavior, tradeoffs, and uncertainty to senior technical and business leaders. Work across the full modeling lifecycle: problem formulation, feature engineering, training, calibration, deployment, monitoring, and iteration in production. Build agentic engineering workflows that accelerate development, testing, and documentation. Collaborate with Product, Engineering, Legal, Compliance, and Operations to ensure credit systems reflect business goals and regulatory expectations. Share modeling context and approaches across teams, helping align how credit risk is measured, interpreted, and discussed. Shape how AI developer tooling is adopted across the team, defining review practices, quality standards, and governance patterns. You Have A Bachelor's degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science). Advanced degrees welcome. 10+ years applying AI, machine learning, or statistical modeling in decisioning contexts such as credit, risk, fraud, recommendations, or similar domains. Experience with probabilistic models and decision systems, including calibration, score transformations, and interpretation of model outputs. Strong experimentation skills: you know how to design holdouts, measure lift, and evaluate models beyond aggregate metrics. Experience with model monitoring, degradation detection, and retraining strategies in production systems. Proficiency with AI-native development workflows. You use LLMs, agentic coding tools, and AI-assisted automation as a regular part of how you build and ship. Experience explaining modeling concepts, results, and limitations to senior stakeholders and cross-functional partners. Experience working across disciplines in environments with meaningful constraints. Technologies We Use and Teach Python (NumPy, Pandas, scikit-learn, PyTorch, XGBoost, LightGBM) AI development tools as core infrastructure: Claude Code, Cursor, Copilot MLflow for experiment tracking and model registry Internal feature store and model hosting platform Prefect and Airflow for orchestration SQL / Snowflake GitHub GCP / AWS 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.

About Us Nomic was founded with a simple but ambitious goal: to make biology easier to measure. We’ve developed nELISA , the world’s highest throughput proteomic platform, by tackling some of the toughest challenges in protein profiling through a combination of DNA nanotechnology, high-dimensional flow cytometry, lab automation, and machine learning. Since spinning out of McGill University, we’ve partnered with dozens of top-tier drug discovery groups, including 6 of the top 10 pharma companies, and have profiled over 60 million proteins from more than 400,000 samples to date. Since closing a $42M Series B round, we recently scaled up the platform to meet rapidly growing demand. You can read more about this on our website here. Our state-of-the-art facility is capable of profiling over 2.5 million samples a year, generating 500 million protein assays. We’re a diverse team of engineers, scientists, and problem-solvers who thrive on breaking down difficult challenges using first principles thinking, and we leverage the latest scientific and technological breakthroughs to drive our mission forward. About the Role As our Staff-level Bioinformatics & ML Scientist, you will elevate Nomic’s position as the leader in large-scale proteomics. Your work will set new standards for how proteomic and multi-omics data are analyzed, interpreted, and applied, helping our customers move from raw data to scientific breakthroughs. By enabling new decision-support models, building and expanding our reference datasets, and sharing thought leadership with the broader community, you will help define what’s possible for the field and accelerate discoveries across drug development and biomarker research. You will also ensure Nomic’s customer collaborations deliver high-impact insights and become the gold standard for industry and academia. You will join a cross-functional team and build on existing infrastructure to develop the analysis pipelines, ML models, and product-facing workflows that make proteomic data actionable for customers. Your work will shape the applications layer in the Nomic Portal and support key use cases such as target discovery, perturbation analysis, and translational research. This is a player-coach position, you will mentor and collaborate closely with our engineering, product and data teams as our capabilities grow. What You’ll Be Doing Bioinformatics, ML, and Multi-omics Analysis Build and improve pipelines for proteomic and transcriptomic data: QC, normalization, batch correction, feature engineering, and integration. Develop ML models and analytical methods for target discovery, perturbation and functional genomics analysis, and phenotype classification. Prototype ML-driven approaches and work with engineering teams to productionize them. Scientific Interpretation & Applications Development Interpret multi-omics datasets and connect data patterns to underlying biology. Support analyses involving perturbation screens or functional genomics methods. Define and translate customer analysis needs into specific Portal features and workflows. Customer-Facing Scientific Insights Support customer projects with exploratory and confirmatory analyses. Identify and communicate the insights most relevant to customer decisions. Present data clearly and rigorously in sharable notebooks, presentations, and discussions. Cross-Functional Collaboration & Thought Leadership Work with product, commercial, and scientific teams to define high-impact use cases. Contribute to scientific content that strengthens Nomic’s leadership. What We’re Looking For PhD (or equivalent experience in Bioinformatics, Computational Biology, ML, or a related quantitative field.) Direct experience analyzing proteomics and transcriptomics datasets Experience working with data from target discovery, perturbation, or functional genomics workflows. 5+ years of hands-on experience building bioinformatics or ML pipelines in Python and/or R. Comfortable staying hands-on with coding and analysis while mentoring others. Strong statistical and data-wrangling skills, including QC and normalization. Familiarity with reproducibility and collaborative coding practices. Experience collaborating with engineers, scientists, or product teams. Experience contributing to scientific publications or technical content. Experience with customer-facing scientific work is ideal. Understanding of how pharma and biotech teams evaluate biological data is ideal. Excellent written and verbal communication skills.

About Us Founded in the US in 2022 and now based in London, UK, Recraft is an AI tool for professional designers, illustrators, and marketers, setting a new standard for excellence in image generation. We designed a tool that lets creators quickly generate and iterate original images, vector art, illustrations, icons, and 3D graphics with AI. Over 3 million users across 200 countries have produced hundreds of millions of images using Recraft, and we’re just getting started. Join a universe of professional opportunities, develop and support large-scale projects, and shape the future of creativity. We are committed to making Recraft an essential, daily tool for every designer and setting the industry standard. Our mission is to ensure that creators can fully control their creative process with AI, providing them with innovative tools to turn ideas into reality. If you’re passionate about pushing the boundaries of AI, we want you on board! About the Role As a Junior Machine Learning Engineer at Recraft, you will have the opportunity to work on real-world AI applications, gaining hands-on experience in model development, data collection, evaluation, and production deployment. You will collaborate with research scientists, engineers, and product teams to help enhance Recraft’s AI-driven creative tools. If you are passionate about machine learning, deep learning, and AI-driven applications, this is a great opportunity to learn and contribute to impactful projects. Key Responsibilities Assist in training, testing, and evaluating machine learning models for real-world applications. Support data collection and processing for model development. Conduct experiments and model evaluations, helping improve accuracy and efficiency. Develop and train large-scale generative models, pushing the boundaries of AI capabilities. Work closely with ML engineers and researchers to implement AI techniques into production workflows. Stay updated with the latest trends in AI and deep learning, contributing fresh ideas to the team. Qualifications The opportunity to work on real-world AI projects in a growing tech company. A collaborative and innovative environment.