Mechanical Engineer & Python Expert - Freelance AI Trainer
Design computational engineering problems to challenge a frontier AI model, ensuring each problem has an answer verifiable by code and requires a specialized tool such as Cantera, CoolProp, CalculiX, OpenFAST, or others. Produce problems that run inside a sealed Linux container with the pre-installed tool and a programmatic judge that grades the model's answer. Select an anchor tool and design a problem focusing on its solvers, simulation kernels, or domain-specific models. Write Python reference solutions, supply input files and geometry or mechanism definitions as needed. Determine the numerical answer and set a domain-appropriate tolerance for correctness. Test and tune the problem against batches of parallel model attempts until the agent's success rate falls within the 10-30% range. Submit the completed task for senior reviewer feedback to ensure high task quality. Calibrate problems by rewriting thermodynamic cycles, adjusting material models and boundary conditions, and monitoring AI agent behaviors to refine problem difficulty. Acquire deeper command of the anchor tool and develop practical intuition for how the AI model handles complex thermal, structural, and fluid mechanics challenges.
Enterprise Account Executive
The AI Outcomes Manager will partner with executive sponsors and end users to identify high-impact use cases and turn them into measurable business outcomes on Glean. They will lead strategic reviews and advise customers on their AI roadmap to ensure maximum value from Glean's platform. Responsibilities include translating business needs into clear problem statements, success metrics, and practical AI solutions, collaborating with Product and R&D to shape priorities, conducting discovery workshops, scoping pilots, and guiding rollouts to drive breadth and depth of adoption of the Glean platform. The role involves designing and building AI agents with and for customers, including rethinking and redesigning underlying business processes to maximize impact and usability. Additionally, the AI Outcomes Manager will proactively identify expansion opportunities and drive engagement across teams and functions.
Senior Manager, Revenue Operations
The AI Outcomes Manager partners with executive sponsors and end users to identify high-impact use cases and turn them into measurable business outcomes on the Glean platform. They lead strategic reviews and advise customers on their AI roadmap to maximize value, translate business needs into clear problem statements, success metrics, and practical AI solutions while collaborating with Product and R&D. They conduct discovery workshops, scope pilots, guide rollouts to drive adoption of the Glean platform, design and build AI agents with and for customers, including rethinking and redesigning underlying business processes to maximize impact and usability, and proactively identify expansion opportunities and drive engagement across teams and functions.
Product Manager, AI Quality
Partner with executive sponsors and end users to identify high-impact use cases and turn them into measurable business outcomes on Glean. Lead strategic reviews and advise customers on their AI roadmap to ensure maximum value from Glean’s platform. Translate business needs into clear problem statements, success metrics, and practical AI solutions while collaborating with Product and R&D to shape priorities. Conduct discovery workshops, scope pilots, and guide rollouts to drive breadth and depth of adoption of the Glean platform. Design and build AI agents with and for customers, including rethinking and redesigning underlying business processes to maximize impact and usability. Proactively identify expansion opportunities and drive engagement across teams and functions.
Tech Lead Manager, Admin Console
The AI Outcomes Manager partners with executive sponsors and end users to identify high-impact use cases and turn them into measurable business outcomes on Glean. They lead strategic reviews and advise customers on their AI roadmap to maximize value from Glean’s platform. The role involves translating business needs into clear problem statements, success metrics, and practical AI solutions, collaborating with Product and R&D to shape priorities. Responsibilities include conducting discovery workshops, scoping pilots, guiding rollouts, and driving the breadth and depth of adoption of the Glean platform. The manager designs and builds AI agents with and for customers, including rethinking and redesigning underlying business processes to maximize impact and usability. They also proactively identify expansion opportunities and drive engagement across teams and functions.
Forward Deployed Engineer - Agents(Remote)
As a Forward Deployed Engineer - Agents, you will lead the end-to-end implementation of AI Virtual Agents and CX automation workflows for customers, owning the entire process from discovery and scoping through launch and optimization. Responsibilities include configuring agent workflows, decision logic, and automation behaviors to maximize accuracy, reliability, and business outcomes; implementing guardrails and validation frameworks to ensure safe, compliant, and predictable agent performance; building, testing, and validating integrations with enterprise systems such as CRM, ticketing, telephony, and data platforms; partnering with customer technical stakeholders to define success criteria, gather requirements, and deliver against timelines; translating customer needs into clear implementation plans and documentation; running tight feedback loops with Engineering and Product to improve platform capabilities; and collaborating with Product, Engineering, Design, and GTM teams to deliver repeatable, best-in-class deployments.
Software Engineer (Brazil)
Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.
Principal Applied AI Researcher - Domain- Specific Models (India)
The Principal Applied AI Researcher is responsible for setting the company-level technical direction for domain-specific model strategy, defining how Articul8 builds, evaluates, scales, and sustains model superiority across continued pre-training, fine-tuning, post-training, and release quality standards. They architect the agentic model development paradigm by designing the agent-orchestrated research infrastructure to enhance research capabilities. The role involves leading deep research on model adaptation methodology, data curation strategies, post-training methods, and training dynamics while deploying agentic systems for exhaustive studies and failure analyses. Additionally, they shape model strategy across all domains and verticals of the company, prioritizing new model domains through agent-driven competitive intelligence and market analysis. They define evaluation strategy, including benchmark design, expert assessments, model failure analysis, and robustness standards, building always-on evaluation systems. The researcher leads cross-cutting research initiatives to strengthen the model layer, influences platform-level decisions about model lifecycle management, portfolio strategy, release criteria, and integration architecture. They mentor senior researchers, coach on agent-augmented research design, and raise technical judgment and rigor. Lastly, they maintain hands-on research impact through publications, patents, and visible output, exemplifying the use of massively parallel agentic systems for groundbreaking research.
Staff Platform Engineer (IND)
Set technical direction for the data platform by owning the architecture roadmap for Fiddler's ingestion, storage, and query layers. Drive multi-quarter initiatives from problem framing through design, implementation, and rollout. Design systems for 10x scale by leading the evolution of the ClickHouse-backed analytics layer and Kafka-based ingestion pipeline to handle significant growth in event volume, query complexity, and tenant count. Define the event model for next-generation AI workloads by architecting the data model and storage strategy for agentic application traces, LLM evaluation pipelines, and enrichment workflows, balancing flexibility, query performance, and schema evolution. Drive cross-team technical decisions by partnering with Backend, Monitoring, and Enrichment teams to ensure platform abstractions meet their needs and represent the Platform perspective in company-wide architecture reviews. Own platform reliability and cost efficiency by establishing SLOs, capacity planning processes, and cost optimization strategies for data infrastructure, and making build-vs-buy decisions for infrastructure components. Raise the engineering bar by mentoring senior engineers and establishing patterns and guardrails including data modeling conventions, query optimization practices, and testing strategies that have team-wide impact. Lead by example in code review, design documentation, and incident response. Influence product direction by working with Product and Customer Engineering to translate customer data challenges into platform capabilities and help define priorities and risks for future work.
Software Engineer, Model Serving Infrastructure
The role involves contributing to the development of next-generation, high-performance machine learning serving systems. Responsibilities include building infrastructure that powers AI applications, working on problems at the intersection of distributed systems, machine learning, and high-performance computing, and solving fundamental computer science problems impacting AI deployment. Specific projects include implementing asynchronous inference for non-blocking client requests, designing intelligent request routing systems to balance load across thousands of model replicas with strict latency SLAs, building traffic management systems for zero-downtime model updates handling terabytes of inference requests, improving state management for scale from thousands to tens of thousands of replicas, architecting frameworks for multi-model orchestration in complex ML pipelines ensuring end-to-end latency guarantees, and developing observability and debugging tools for distributed ML applications at scale. The work involves writing performance-critical code in Python (with Cython optimizations) and potentially C++, working with distributed systems at scale using Ray Core's actor system, gRPC, and custom networking protocols, extending cloud-native infrastructure such as Kubernetes and service meshes, gaining system-level knowledge of ML/AI frameworks like TensorFlow, PyTorch, JAX, and transformers, and ensuring production reliability with tools like OpenTelemetry, Prometheus, distributed tracing, and chaos engineering to maintain 99.99% uptime. The role also involves leveraging AI coding agents to enhance team productivity while maintaining high code quality standards.
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