Role Description As a Senior Machine Learning Engineer on the Data Mining team, your mission is to build the "Brain" of our ML-powered multimodal data mining framework, Omnitag. You will: - Architect and Train Distilled Models: - Design and implement teacher-student model frameworks for multimodal sensor data. - Develop training pipelines for knowledge distillation. - Ensure student models maintain high accuracy while drastically reducing inference latency and memory footprint. - Reinforcement Learning for Data Discovery: - Build RL-based policy learning and reasoning systems for autonomous driving applications. - Implement and scale RL training workflows (e.g., PPO, DQN, actor-critic methods) for simulation and real-world interaction. - Explore reward shaping, environment modeling, and multi-agent RL where applicable. - Optimize Model Deployment for Real-Time Inference: - Collaborate with backend engineers to deploy distilled and RL models into production. - Optimize for latency, throughput, and hardware efficiency across GPU/CPU clusters. - Implement model versioning, A/B testing, and monitoring for performance regressions. - Research and Integrate Agentic Systems: - Explore and prototype agentic workflows for autonomous reasoning, chain-of-thought prompting, and goal-directed behavior. - Integrate such systems into our broader autonomy stack as experimental or production components. - Drive Production Reliability: - Establish patterns for graceful degradation, fault tolerance, and cost optimization. - Operate Omnitag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence. - Mentor and Collaborate: - Work closely with ML scientists, data engineers, and autonomy teams to translate research advances into scalable engineering solutions. - Guide junior engineers in best practices for model training, evaluation, and deployment. Qualifications - BS in Computer Science, Machine Learning, or related field, or equivalent professional experience. - 6+ years of hands-on experience in machine learning engineering, with a focus on model post-training, optimization, and deployment. - Strong experience with model distillation or teacher-student training - practical knowledge of loss functions, training strategies, and evaluation of compressed models. - Proven experience with reinforcement learning in production or research settings: policy optimization, reward design, simulation environments, and RL-based reasoning. - Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX). - Strong software engineering fundamentals: testing, CI/CD, containerization, and system design. - Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for inference. - Demonstrated ability to ship production-grade ML systems and mentor team members. Requirements - Demonstrated track record of shipping robust, well-tested, production-grade systems and mentoring junior engineers. Benefits - Medical, dental, vision. - 401k with a company match. - Health saving accounts. - Life insurance. - Pet insurance. - And more. Salary Range $172,000 — $229,000 USD

• Optimize our pricing models to significantly reduce infrastructure costs while maintaining and improving their accuracy, especially for high-value assets. • Iterate on our underwriting model to maximize cash advance disbursements while maintaining target risk thresholds and default rates. • Lead the full ML lifecycle from model training and feature generation to production deployment and monitoring. • Collaborate closely with our Expert Pricers to become a domain expert in the trading card market and inform model improvements. • Design and execute experiments and backtesting to discover and validate new features that improve the models’ predictive power and coverage. • Own the models’ AWS infrastructure, writing code for our pricing APIs to ensure the models can serve at scale and with low latency.

Role Description At Block, we believe product quality is foundational to great user experiences, and AI is transforming how we measure, understand, and improve that quality at scale. Our team builds the intelligence layer that evaluates system behavior across millions of real-world interactions, helping ensure our products are reliable, safe, and continuously improving. We’re looking for a Senior Machine Learning Engineer to lead the technical direction of next-generation quality systems powered by LLMs and AI agents. You’ll drive the architecture and strategy behind systems that evaluate product behavior, surface emerging issues, generate actionable insights, and enable teams across Block to make higher-confidence product decisions. In this role, you’ll operate across ambiguous, high-impact problem spaces and shape how quality is measured and operationalized across the organization. You’ll work across engineering, product, platform, and leadership teams to define long-term technical direction, establish scalable evaluation frameworks, and build systems that become foundational infrastructure for AI-driven product quality. You Will - Lead the technical strategy and architecture for AI-driven quality and evaluation systems used across products and teams. - Drive the development of scalable systems that use LLMs, agents, and behavioral signals to evaluate quality, detect regressions, and generate product insights. - Define long-term approaches for evaluation, measurement, and quality intelligence across complex product surfaces. - Translate ambiguous organizational needs into clear technical direction, roadmap priorities, and platform capabilities. - Influence engineering standards and best practices for building reliable, measurable, and trustworthy AI systems. - Lead complex cross-functional initiatives spanning product, infrastructure, data, and applied AI teams. - Mentor and level up engineers across the organization through technical leadership, design reviews, and systems thinking. - Identify leverage opportunities where AI systems can fundamentally improve how teams understand, debug, and improve product behavior. Qualifications - 5+ years of experience in software engineering, machine learning engineering or applied AI. - Deep experience designing and shipping large-scale AI/ML systems in production environments. - Strong expertise with LLMs, agents, evaluation systems, retrieval architectures, and modern AI infrastructure. - Proven ability to lead ambiguous, high-impact technical initiatives from concept through adoption across multiple teams. - Strong systems thinking and architectural judgment, with the ability to balance experimentation, scalability, and operational rigor. - Experience defining technical strategy and influencing roadmaps beyond your immediate team. - Excellent communication and cross-functional leadership skills, with the ability to align engineering, product, and organizational priorities. - A track record of creating leverage through platforms, frameworks, and systems that enable other engineers and teams to move faster and make better decisions. Benefits - Remote work - Medical insurance - Flexible time off - Retirement savings plans - Modern family planning Company Description 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.

• Lead the MLOps strategy on Databricks, defining architecture and standards; • Design and review CI/CD pipelines for ML models (training, packaging, deployment, rollback); • Define practices for model versioning and registry, feature stores, experiment reproducibility, and serving; • Establish end-to-end observability: performance, data/model drift, data quality, and business metrics; • Review PRs and critical architecture decisions; mentor the squad on the most complex topics; • Ensure governance, security, and cost control of the data and AI platform; • Participate in key ceremonies (planning, technical refinements, checkpoints), optimized for a part-time workload.