> Markdown version of [/jobs/ext/112090-cognitive-analytics-engineer](https://www.wearedevelopers.com/jobs/ext/112090-cognitive-analytics-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Cognitive Analytics Engineer - **Company:** Corteva - **Location:** Des Moines, IA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, C++ (Programming Language), Linux, Distributed Computing Environment, Python (Programming Language), Machine Learning, Performance Tuning, Reinforcement Learning, Digital Twin, Pytorch, Large Language Models, Multi-Agent Systems, Deep Learning, Kubernetes, Information Technology, Docker - **Published:** May 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=c551bda472d34db1 ## About the Role Do you have experience in Reinforcement learning?, Do you have a Master's degree?, * Master's degree in computer science, machine learning, or a related field (or equivalent practical experience), with 3-5 years of relevant experience * Strong Python skills with computational and scientific libraries * Experience with deep learning frameworks (e.g. PyTorch, JAX) * Experience with reinforcement learning frameworks and theory * Experience with simulation environments or digital twin systems * Experience with deployment of orchestrated agents and associated components (e.g. MCP, ACP) * Experience with distributed training and efficient AI orchestration * Strong communication skills - able to bridge AI/ML concepts with domain scientists and cross-functional teams * Experience with Linux and Docker What Makes You Stand Out: * Experience with LLM-based agent architectures, tool-use orchestration, or multi-agent systems * Experience designing or building custom reinforcement learning environments for real-world decision problems * AWS/Kubernetes experience * Experience with compiled languages (e.g. C++, Rust) for performance optimization * Background in plant breeding, genetics, agriculture, or related domains - candidates with reinforcement learning and simulation backgrounds in other complex decision domains (robotics, operations research, autonomous systems) are encouraged to apply ## Description We're looking for a Cognitive Analytics Engineer to join our Artificial Intelligence in Breeding group. You'll work at the intersection of agentic AI systems, simulation, and reinforcement learning - building and deploying the intelligent systems that enable breeding teams to optimize complex, multi-stage decisions through digital twin environments and AI-driven orchestration. This role is central to our cognitive analytics strategy: you'll help build and deploy the agentic systems and digital twin environments that enable teams to seamlessly integrate simulation, reinforcement learning, and AI-powered decision tools into breeding pipelines and workflows. You'll partner with scientists and engineering stakeholders to deploy these systems to our breeding teams across crops and geographies. Who You Are: You're a systems-minded AI engineer who thinks natively in terms of agents, environments, and optimization. You're comfortable building the infrastructure that makes agentic workflows reliable and scalable, and you have the theoretical grounding to reason about reinforcement learning formulations, simulation fidelity, and reward design. You thrive in collaborative, cross-disciplinary teams and can translate between ML research and production engineering. What You'll Do: * Build and maintain agentic AI systems that orchestrate analytics, simulation, and decision workflows for breeding pipelines, guiding solutions from prototype through production deployment * Implement and optimize digital twin components for use with agentic inference systems and reinforcement learning training pipelines * Develop and train reinforcement learning agents that optimize breeding pipeline strategy * Collaborate with scientists and domain experts to address breeding challenges with agentic-driven solutions leveraging simulation and deterministic optimization * Architect for scale and deployment - distributed training, efficient orchestration, and production-grade reliability of AI systems ## Related Videos - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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