ML Engineer

Check out 1962 new Machine Learning Engineer opportunities posted on AI Chopping Block Design, build, and maintain scalable machine learning systems including data ingestion, preprocessing, training, testing, and deployment. Develop and optimize end-to-end ML pipelines encompassing data collection, labeling, training, validation, and monitoring to ensure reliability and reproducibility. Implement robust MLOps practices such as model versioning, experiment tracking, CI/CD for machine learning, and continuous monitoring in production environments. Collaborate with product and engineering teams to integrate and deploy models into real-time products with a focus on efficiency and scalability. Ensure data quality, observability, and performance across all AI systems. Stay current with the latest AI infrastructure, tooling, and research to support ongoing innovation. Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices and incorporate them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency. Machine Learning Enginer, Core Evaluations The responsibilities include designing model evaluation pipelines for models in both development and production environments, designing user studies for subjective model evaluations, converting requirements into measurable metrics, and designing and developing automated evaluation dashboards to monitor and compare model performance. It also involves training new models to capture various evaluation metrics, communicating with the model team to help design improved models based on evaluation results, coordinating with the data team to determine necessary data for enhancing model performance, collaborating with the product manager to ensure product requirements are accurately measured, helping to grow the evaluation team as the founding member, and leading the evaluation team in the future. Member of Engineering (Reinforcement Learning Infrastructure) Keep up with the latest research, and be familiar with the state of the art in LLMs, RL, and code generation. Develop methods for tuning training and inference end-to-end for high throughput. Design data control systems in an RL pipeline that govern what the model sees and when. Debug cases where infrastructure decisions are silently degrading learning dynamics. Build observability tooling that surfaces when a system-level issue is the root cause of a training regression. Help build robust, flexible and scalable RL pipelines. Optimize performance across the stack — networking, memory, compute scheduling, and I/O. Write high-quality, pragmatic code. Work in the team: plan future steps, discuss, and always stay in touch. Member of Engineering (Reinforcement Learning) Research and experiment on ways to improve reasoning and code generation for LLMs. Own the full experiment life cycle from idea to experimentation and integration. Keep up with the latest research, and be familiar with the state of the art in LLMs, RL, and code generation. Translate research ideas into clean, reusable codebases that other researchers can build on. Design, analyze, and iterate on data generation and training of LLMs. Implement and iterate on RL training pipelines that scale reliably across domains. Diagnose training instabilities and failures, debug RL runs and propose mitigation methods. Write high-quality, reproducible and maintainable code. Research Infrastructure Engineer, Training Systems Build and maintain infrastructure for large-scale model training and experimentation. Design APIs and interfaces to simplify complex training workflows and prevent misuse. Improve reliability, debuggability, and performance of training and data pipelines. Debug issues across technologies including Python, PyTorch, distributed systems, GPUs, networking, and storage. Write tests, benchmarks, and diagnostics to detect significant regressions. The Engineering Manager, AI & Data Infrastructure The Engineering Manager, AI & Data Infrastructure leads the AI & Data Infrastructure team responsible for the data and inference systems that support agent interactions, including streaming and batch pipelines for analytics and customer telemetry, realtime databases for low-latency behavior, and GPU and model-serving platforms for LLM inference. This role involves building, leading, and developing a high-performing team of data and ML infrastructure engineers through hiring, coaching, and performance management. Responsibilities include owning the technical strategy and roadmap for AI & Data Infrastructure, staying hands-on with design and code reviews, leading architecture for high-throughput data systems and low-latency inference, setting reliability, quality, and cost standards, investing in developer and analyst experience, raising standards on AI-assisted engineering practices, and partnering with Research, Product Engineering, Platform, and customer-facing teams to deliver data and inference capabilities, including enterprise deployments. #J-18808-Ljbffr