Research Engineer
Research Engineers at Cohere Labs are responsible for building experiments, debugging models, scaling training pipelines, and turning research ideas into working systems. They work closely with scientists and other engineers to implement new methods, run large-scale experiments, and help shape the infrastructure that supports research programs. The role involves writing clean, reliable code and building systems that others can use and extend, experimenting, running ablations, analyzing results, and iterating quickly. They collaborate closely with researchers to translate ideas into practical implementations and contribute to defining how ideas become reality within the AI research environment.
Model Policy Manager, Chemical & Biological Risk
Design model policies that govern safe model behavior in an objective and defensible way, including determining how models should respond in risky or unsafe scenarios and defining what constitutes unsafe behavior while balancing safety and beneficial model capabilities. Develop taxonomies to inform data collection campaigns, model behavior, and monitoring strategies, balancing utility maximization with catastrophic risk prevention. Lead prioritization of safety efforts across the company for new model launches, addressing technical and business trade-offs. Develop a broad range of subject matter expertise while maintaining agility across various topics. Collaborate with many internal teams requiring high organizational acumen and confident decision making.
Senior Research Engineer
As a Senior Research Engineer at Decagon, you will be responsible for building industry-leading conversational AI models, taking them from idea to production. Your role includes leading research and engineering efforts to improve core conversational capabilities in production such as instruction following, retrieval, memory, and long-horizon task completion. You will build and iterate on end-to-end models and pipelines focusing on quality, efficiency, and user experience. Collaboration with platform and product engineers to integrate new models into production systems is essential. Additionally, you are expected to break down ambiguous research ideas into clear, iterative milestones and roadmaps.
Applied Research - Team Lead
Lead the research, development, and deployment of AI agents for production systems. Collaborate closely with engineering and product teams to integrate AI capabilities into the product experience. Drive the full life-cycle of AI systems from conception through deployment, including building robust evaluation frameworks to measure and improve agent performance. Stay at the forefront of AI by exploring the latest advancements in agents, evals, and applied AI research. Provide strategic direction and mentorship while contributing directly to prototyping, building, and iterating on AI systems.
Product Manager Intern
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 by designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation 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.
UX Research Intern
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 by designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation 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 to manipulate complex or deformable objects with high precision. Work with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in 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.
AI Research Engineer
Design and implement multi-agent and reinforcement learning (RL) approaches for agentic code generation and tool-use. Build research prototypes that integrate with nectar and collaborate to productionize successful results. Create evaluation suites including task specifications, pass/fail checkers, coverage, and cost/latency dashboards. Acquire and curate datasets from PDFs, logs, tables, and generate synthetic data when appropriate, while maintaining data cards and licensing. Analyze experiments using disciplined ablations, document results and decisions. Stay current on developments in LLM agents, RL (offline/online, RLHF/RLAIF), constrained decoding, and program synthesis.
Applied Legal Researcher
Develop and deliver subject-matter expertise to support AI research; work closely with engineering, product, and design teams to define and develop AI systems; build and improve AI systems through prompt engineering, fine tuning, and other techniques; build proprietary benchmarks and datasets to evaluate models and model systems; partner directly with clients to understand their workflows, identify pain points, and translate complex business and legal requirements into technical solutions.
Applied Legal Researcher
Develop and deliver subject-matter expertise to support AI research; work closely with engineering, product, and design teams to define and develop AI systems; build and improve AI systems through prompt engineering, fine tuning, and other techniques; build proprietary benchmarks and datasets to evaluate models and model systems; partner directly with clients to understand their workflows, identify pain points, and translate complex business and legal requirements into technical solutions.
Research Scientist, Human AI Interaction
As a Research Scientist, Human–AI Interaction, you will lead research at the intersection of Human–Computer Interaction, Large Language Models, and task-level benchmarking to define how AI systems support real human work. Responsibilities include leading research on jobs-to-be-done benchmarks for AI systems by defining task taxonomies grounded in real professional and economic activities, identifying meaningful task completion criteria, and translating qualitative work understanding into measurable benchmarks. Develop methods to measure human activity in AI-mediated workflows and design benchmarks to assess AI-as-a-collaborator/copilot. You will design and run empirical studies involving controlled experiments and field studies to measure task performance and capture fine-grained interaction traces, drive strategy for professional-domain AI benchmarks based on understanding domain-specific workflows, and build and prototype AI systems and evaluation infrastructure including LLM-powered copilots, benchmark harnesses, data pipelines, and human-in-the-loop evaluation interfaces. Collaborate closely with User Experience Research to leverage qualitative insights and translate ethnographic findings into formal research constructs. Publish and present your research regularly to advance the field of human-centered AI benchmarking at top-tier conferences.
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