Legal Advisor (US Bar Admitted) - Freelance AI Trainer
Contributors may generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.
Legal Advisor (US Bar Admitted) - Freelance AI Trainer
Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.
Legal Advisor (US Bar Admitted) - Freelance AI Trainer
Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.
Legal Advisor (US Bar Admitted) - Freelance AI Trainer
Contributors generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.
Legal Advisor (US Bar Admitted) - Freelance AI Trainer
Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.
Staff ML Systems Engineer, Distributed Systems
Design and build scalable distributed machine learning pipelines across data processing, model training, evaluation, and post-processing workflows. Architect distributed execution systems, including parallelization strategies, workload scheduling, resource allocation, and fault tolerance mechanisms. Develop reusable abstractions, frameworks, and libraries that simplify distributed pipeline development. Optimize performance across distributed CPU and GPU environments, improving throughput, utilization, and reliability. Design systems that effectively manage data partitioning, memory utilization, serialization overhead, and compute efficiency. Partner closely with ML engineers, data engineers, and infrastructure teams to productionize research workflows and enable large-scale model development. Establish best practices and engineering standards for distributed machine learning infrastructure. Evaluate and guide decisions around distributed computing frameworks, infrastructure technologies, and system design trade-offs. Improve observability, debugging, monitoring, and operational tooling for distributed systems at scale.
Optical Engineer - Freelance AI Trainer
Contributors may design original optics problems that simulate real physics research workflows, ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes, develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics, base problems on real research challenges or practical applications from optics and physics practice, and document problem statements clearly with verified correct answers.
Client Engineering Lead
As a Staff/Principal-level Technical Lead, you will be responsible for driving the end-to-end technical execution of multiple concurrent enterprise engagements in close partnership with the Project Lead, from technical discovery to production deployment. You will architect and implement secure, highly scalable integrations between the AI platform and clients' existing data pipelines, APIs, and infrastructure. You will lead technical discovery sessions, architecture workshops, and data readiness assessments with customer IT, data, and engineering leadership teams. You will build and customize AI-enabled solutions, scripts, and workflows that address complex business problems identified in the sales process. You will serve as the primary technical liaison and escalation point between customer engineering teams and internal product, engineering, and data science teams to unblock deployments quickly. You will ensure that all deployed solutions meet enterprise-grade standards for performance, security, data privacy, and scalability. You will debug complex integration issues, manage technical risks across overlapping projects, and provide hands-on troubleshooting during implementation. Additionally, you will contribute to the internal codebase by documenting technical blueprints, developing reusable integration components, and providing product feedback based on real-world edge cases.
Software quality engineer (US)
Define and implement comprehensive quality assurance strategies and test plans for AI agents and LLM-powered applications to ensure product reliability and performance. Design and develop automation frameworks, creating robust, scalable, and maintainable automated test frameworks or enhancing existing ones using languages such as Typescript and Python. Collaborate with product managers, machine learning engineers, and data scientists to understand AI features and model behaviors, translating these into test cases and validation criteria. Drive continuous improvement of testing processes and infrastructure by integrating automated checks within CI/CD pipelines for rapid, high-quality releases. Identify, document, and track software defects and inconsistencies, performing root cause analysis to provide actionable feedback to development teams. Monitor production systems and AI model performance to identify potential issues and contribute to post-release quality validation. Champion quality best practices across engineering teams, fostering a culture of ownership and continuous improvement. Design, manage, and maintain test data strategies and mock services to ensure stable, isolated, and repeatable test execution. Design, develop, or integrate agentic AI systems, AI skills, and the Model Context Protocol (MCP). Manage the full defect lifecycle by analyzing customer feedback and debugging logs to identify, prioritize, and track software bugs, collaborating with development teams to ensure timely resolution.
Supervisor, HCS Specialty Recovery
Design agent systems from first principles by deciding the loop, tools, context strategy, and evaluation method, choosing between different architectural approaches and defending choices with data; engineer the context including prompt design, context windows, tool surfaces, structured outputs, and citation grounding; drive evaluation rigor by building evaluations prior to agent development, diagnosing failures, fixing root causes, and proving improvements; use AI tools such as Claude Code and Codex extensively for planning, scaffolding, refactoring, and debugging; and become a domain expert in healthcare claims, coding guidelines, and medical records to contribute effectively.
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