Parcel Contract Intelligence Consultant
Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if a better, faster solution exists. Work across system boundaries with full-stack proficiency, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate work, focusing on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.
Mathematics & Python Expert - Freelance AI Trainer
Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly while providing verified correct answers.
Forward Deployed Engineering Manager
As an Applied Research Engineer at Labelbox, responsibilities include developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers, designing and implementing advanced systems that align human feedback into AI training processes such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), working on innovative techniques to measure and improve human data quality, developing AI-assisted tools to enhance the data labeling process, advancing AI alignment methods to better reflect human preferences, improving human-in-the-loop data quality through rigorous measurement and enhancement systems, increasing efficiency and effectiveness in AI-assisted data labeling by creating tools using active learning and adaptive sampling, investigating the impact of different types of human feedback on model performance and alignment, optimizing human feedback collection with novel algorithms, integrating research breakthroughs into Labelbox’s product suite, engaging with customers and the AI community to understand data needs and share best practices, publishing in top-tier journals and conferences, continuously exploring new frontiers in human-AI collaboration and AI alignment, and establishing technical documentation and educational content to influence human-centric AI development.
Forward Deployed Research Scientist
As an Applied Research Engineer at Labelbox, the role involves developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. Responsibilities include designing and implementing advanced systems to align human feedback into AI training processes such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). The role requires working on innovative techniques to measure and improve human data quality, developing AI-assisted tools to enhance the data labeling process, advancing AI alignment by creating new methods that better reflect human preferences, and increasing efficiency in AI-assisted data labeling through active learning and adaptive sampling. Additionally, the role involves investigating the impact of different types of human feedback on model performance and alignment, optimizing human feedback collection algorithms, integrating research breakthroughs into Labelbox's product suite, engaging with customers and the AI community to understand evolving data needs, publishing research in top-tier journals and conferences, and creating technical documentation and educational content to establish Labelbox as a thought leader in human-centric AI development.
Senior Robotics & Software Engineer - Grippers R&D (202648)
Building the best-in-the-world objects manipulation system with a cross-functional R&D team including Hardware, Software, and AI; inventing and implementing strategies for using new robotic grippers to handle previously unpickable items; improving adaptability, scalability, and reliability of the robotic platform; using data to build heuristics for handling different categories of items; and detecting anomalies using a combination of signals to determine if a robot picked more than one item at once or if one item is disassembling.
Member of Technical Staff (Data): World Models
Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.
Forward Deployed Engineer, RL Environments
As an Applied Research Engineer at Labelbox, the responsibilities include developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. The role involves designing and implementing advanced systems aligning human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO). Responsibilities also include working on innovative techniques to measure and improve human data quality, developing AI-assisted tools to enhance data labeling, investigating the impact of different types of human feedback on model performance, optimizing human feedback collection algorithms, integrating breakthroughs into Labelbox's product suite to make human-AI alignment techniques scalable, engaging with customers and the AI community to understand evolving data needs, publishing research in top-tier journals and conferences, exploring new frontiers in human-AI collaboration and AI alignment, and creating technical documentation and educational content to establish Labelbox as a thought leader in AI.
Machine Learning Intern (202641)
As a Machine Learning Intern at Nomagic, you will dive into complex problems of physical manipulation to enhance robot capabilities. Your responsibilities include expanding the perception abilities of the robotic system to handle a wider variety of products, detecting anomalies such as identifying when a robot picks more than one item or when an item is disassembling, training models to solve multiple problems with various loss functions, and productionizing machine learning models which involves performance monitoring and A/B testing. You will work on developing groundbreaking technology and collaborate with top professionals in an English-speaking environment, with opportunities to play with robots daily and contribute directly to impactful results.
Senior Product Engineer AI (remote, UTC-3 to UTC+3)
Design and build AI agents and AI-enhanced features iteratively that help customers debug, fix, and create Playwright and API tests faster. Implement solutions full stack with your team. Get in touch with users to learn from their feedback directly to build solutions that are delightful and solve real problems.
Electrical Engineer & Python Expert - Freelance AI Trainer
Contributors may design rigorous electrical engineering problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy); improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.
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