Member of Technical Staff, Senior/Staff MLE
Lead the design and delivery of custom LLM solutions for enterprise customers, translating ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methodologies. Build custom models using Cohere's foundation model stack, CPT recipes, post-training pipelines (including RLVR), and data assets. Develop SOTA modeling techniques that enhance model performance for customer use-cases and contribute improvements back to the foundation-model stack, including new capabilities, tuning strategies, and evaluation frameworks. Work closely with enterprise customers to identify high-value opportunities for LLMs and provide technical leadership throughout discovery, scoping, modeling, deployment, agent workflows, and post-deployment iteration. Establish evaluation frameworks and success metrics for custom modeling engagements. Mentor engineers across distributed teams, drive clarity in ambiguous situations, build alignment, and raise engineering and modeling quality across the organization.
Member of Technical Staff, MLE
As a Member of Technical Staff, Applied ML, you will work directly with enterprise customers to understand their domains, design custom LLM solutions, and deliver production-ready models that solve high-value, real-world problems. You will train and customize frontier models using Cohere’s full stack, including CPT, post-training, retrieval and agent integrations, model evaluations, and state-of-the-art modeling techniques. You will influence the capabilities of Cohere’s foundation models by developing techniques, datasets, evaluations, and insights that shape the next generation of models. Your responsibilities include contributing to the design and delivery of custom LLM solutions for enterprise customers, translating ambiguous business problems into well-framed ML problems with clear success criteria and evaluation methods, building custom models using the foundation model stack and post-training pipelines, developing state-of-the-art modeling techniques, contributing improvements back to the foundation model stack including new capabilities and evaluation frameworks, and working as part of the customer-facing MLE team to identify high-value opportunities where LLMs can unlock transformative impact for enterprise customers.
Evaluation Scenario Writer - AI Agent Testing Specialist
Design realistic and structured evaluation scenarios for LLM-based agents by 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.
Staff/Senior AI/ML Engineer - (Dublin, CA)
Design, develop, and deploy AI/ML models ranging from traditional ML regression algorithms to transformer-based architectures. Train, fine-tune, and optimize deep learning and LLM-based solutions. Engage with customers to understand their needs and transform them into actionable tasks for developing new functionalities within the Articul8 platform. Collaborate with researchers, software engineers, and product teams to integrate new AI capabilities into applications. Implement and evaluate state-of-the-art automated testing and metrics to improve model accuracy and efficiency. Optimize models for both cloud and on-premises environments to ensure low latency and high availability. Develop APIs and micro-services to serve AI models in production. Stay current with the latest AI models, research, and best practices. Ensure ethical AI practices, data privacy, and security compliance.
Machine Learning Engineer (AI detection, Toronto)
Design, train, and fine-tune state-of-the-art language models; develop AI agents combined with retrieval-augmented language models; build efficient and scalable machine learning training and inference systems; stay up-to-date with the latest literature and emerging technologies to solve novel problems; work closely with product and design teams to develop intuitive applications that create societal impact.
Senior AI/ML Engineer
The Senior AI/ML Engineer is responsible for designing and implementing autonomous agents capable of task decomposition, reasoning, and self-correction, building systems that enable complex multi-step agentic workflows. They develop robust interfaces for large language models (LLMs) to interact with external APIs, databases, and financial tools, ensuring reliable function calling and accuracy within the spend-to-pay ecosystem. They lead the integration of advanced LLMs, focusing on Retrieval-Augmented Generation (RAG) and long-term memory management for high-stakes financial decision-making. Additionally, they architect and manage MLOps pipelines including continuous integration, continuous delivery (CI/CD), model serving, monitoring, and automated retraining to ensure the reliability, scalability, and efficiency of ML services. They also collaborate cross-functionally with product managers, software engineers, and data scientists to translate business requirements into technical solutions and integrate AI/ML models into core platforms.
AI/ML Manager - Engineering Leader
Lead, mentor, and grow a high-performing team of AI/ML engineers, fostering a culture of innovation, technical excellence, and continuous learning. Collaborate cross-functionally with Customer Success, Product Management, Engineering, and Business Development to scope, prioritize, and align AI/ML initiatives with core business objectives. Define and enforce best practices for the full ML lifecycle, including experimentation, code reviews, reproducibility, deployment pipelines, monitoring, and MLOps. Own the technical roadmap for AI/ML capabilities, ensuring alignment with long-term product strategy while rapidly adapting to research findings and market shifts. Drive translation of applied research into production-ready solutions, balancing cutting-edge innovation with pragmatic delivery at startup speed. Establish team processes for prioritization, planning, and technical guidance to optimize execution speed while ensuring reliability, scalability, and quality. Promote a data-driven culture by defining success metrics and KPIs, ensuring technical outputs are measurable, impactful, and tied to business outcomes. Contribute hands-on to technical architecture, model design, and code reviews where appropriate, while balancing technical leadership and management responsibilities. Advocate for responsible and ethical AI practices, ensuring compliance with organizational policies and industry standards.
Head of Machine Learning (Remote - UK/Europe)
The Head of Machine Learning will manage 9 Machine Learning Engineers, including 3 Team Leaders, with responsibilities spanning People Management and project coordination. They will understand and coordinate the strategic direction of ML team projects, manage dependencies, allocate resources, and ensure alignment with business and product goals. This includes contributing to system architecture and development by empowering the team via 1:1s, code reviews, and discussions to deliver impactful features. The role involves leading and nurturing the ML engineering team through coaching and mentorship, leading team OKR discussions, coordinating projects, facilitating meetings, and collaborating with the CTO, Platform, and Product Managers to align team priorities with company OKRs. They will work with the People team on recruiting and onboarding talent, act as a sounding board for the team, support identifying and resolving bottlenecks and blockers to enable faster iteration, drive ML system development and deployment, optimize tools and infrastructure for efficiency, and promote a culture of collaboration and continuous learning while mentoring team members.
Senior/Staff Machine Learning Engineer - Perception Offline Driving Intelligence
As an engineer in the Offline Driving Intelligence (ODIN) team at Zoox, the responsibilities include developing advanced multimodal large language models to enhance environmental understanding for robotaxis, designing model architectures and training techniques using sensor inputs and large scale data, driving end-to-end machine learning solutions from research to production using Zoox's data pipelines and infrastructure, collaborating with perception, planning, safety, and systems teams to integrate models into the vehicle's decision-making pipeline, and validating and optimizing solutions using real-world driving scenarios to contribute directly to the safety and reliability of Zoox's autonomous system.
Senior Machine Learning Engineer - Simulation Scenario Generation
Contribute to tooling for AI-based scenario understanding and validation. Synthesize realistic autonomous vehicle simulation scenarios with dynamic (e.g., traffic) and static features. Integrate and validate large language models (LLMs), vision-language models (VLMs), and implement other models for complex scenario generation workflows, leveraging techniques like agentic tool use. Collaborate directly with internal customers and partner teams to provide generative AI solutions for their test creation workflows. Directly contribute to the safety and reliability of Zoox's autonomous software.
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