Software Engineer, macOS Core Product - Singapore, Singapore
Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for various use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.
Mechanical Engineer with Python Experience - Freelance AI Trainer
Design graduate- and industry-level mechanical engineering problems grounded in real practice. Evaluate AI-generated solutions for correctness, assumptions, and engineering logic. Validate analytical or numerical results using Python (NumPy, SciPy, Pandas). Improve AI reasoning to align with first principles and accepted engineering standards. Apply structured scoring criteria to assess multi-step problem solving.
Electrical Engineer with Python Experience - Freelance AI Trainer
The role involves designing rigorous electrical engineering problems that reflect professional practice, evaluating AI solutions for correctness, assumptions, and constraints, validating calculations or simulations using Python (including libraries such as NumPy, Pandas, SciPy), improving AI reasoning to align with industry-standard logic, and applying structured scoring criteria to multi-step problems.
Enterprise Account Executive - Italy
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. The role involves translating business needs into clear problem statements, success metrics, and practical AI solutions while collaborating with Product and R&D to shape priorities. They will conduct discovery workshops, scope pilots, and guide rollouts to drive broad and deep adoption of the Glean platform. Additionally, they will design and build AI agents with and for customers, including rethinking and redesigning underlying business processes to maximize impact and usability. The manager will proactively identify expansion opportunities and drive engagement across teams and functions.
Senior AI Engineer - San Mateo, CA
The role involves training, evaluating, and monitoring new and improved LLMs and other algorithmic models. The engineer will test and deploy content moderation models in production and iterate based on real-world performance metrics and feedback loops. They are expected to develop medium to long-term vision for content understanding-related R&D, collaborating with management, product, policy & operations, and engineering teams. The position requires taking ownership of results delivered to customers, advocating for changes in approach where needed, and leading cross-functional execution.
Evaluation Scenario Writer - AI Agent Testing Specialist
Design realistic and structured evaluation scenarios for LLM-based agents, creating test cases that simulate human-performed tasks and defining gold-standard behavior to compare agent actions against. Create structured test cases that simulate complex human workflows. Define gold-standard behavior and scoring logic to evaluate agent actions. Analyze agent logs, failure modes, and decision paths. Work with code repositories and test frameworks to validate scenarios. Iterate on prompts, instructions, and test cases to improve clarity and difficulty. Ensure scenarios are production-ready, easy to run, and reusable.
MCP & Tools Python Developer - Agent Evaluation Infrastructure
Developing and maintaining MCP-compatible evaluation servers, implementing logic to check agent actions against scenario definitions, creating or extending tools that writers and QAs use to test agents, working closely with infrastructure engineers to ensure compatibility, and occasionally helping with test writing or debug sessions when needed.
Solutions Architect
As a Solutions Architect at Cohere, you will develop and deliver cutting-edge agentic AI solutions using Cohere’s foundation models and Agentic AI Foundry - North. You will architect scalable, secure, and customizable NLP and generative AI solutions tailored to enterprise customer needs. You will collaborate with customers to understand complex workflows, design pilots, and translate business requirements into technical solutions including model fine-tuning, custom agents, and agent orchestration. You will support the deployment and integration of large language models and custom solutions into production environments using Kubernetes, Docker, and cloud infrastructures, ensuring high performance and security. You will lead technical engagements such as deep dives into AI architectures, facilitate workshops, and establish best practices for agent-based AI systems and model customization. Additionally, you will work with product development to provide customer feedback on agentic AI capabilities, contribute to product enhancements, and help shape future features. You will serve as the technical relationship owner, owning the customer narrative, acting as the voice of the customer, and liaising between customers and the product team, providing guidance on best practices for using Cohere, identifying platform improvement areas, and cultivating technical champions within customer organizations to drive adoption and gather feedback to enhance products.
Freelance Accounting Consultant - AI Trainer
You will create complex, realistic tasks that push frontier AI agents to their limits, dealing with scattered data, conditional procedures, and requiring genuine domain expertise. You will build a detailed version of tasks with objective scoring and write an ambiguous version aimed at training the AI agent to succeed with less guidance. This role involves improving the AI tools for future use, creating training prompts, and refining model responses. The work is project-based, flexible, and involves learning to test and evaluate modern AI systems while working asynchronously and remotely.
Senior Cloud Operations Engineer
Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities deployable on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms that enable robots to manipulate complex or deformable objects with high precision. Collaborate with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in both simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.
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