Applied AI Engineer Jobs

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Design and develop AI applications primarily in Python. Run evaluations to validate models and package solutions for Kubernetes, AWS, or adapt them to customer on-premises clusters. Lead discovery sessions, guide pilot projects, and ensure successful deployments. Collaborate mostly remotely with occasional on-site workshops. Monitor system performance and reliability. Add to the logging, billing and auth services. Build internal tooling to automate repetitive tasks. Provide feedback on patterns, pain points, and reusable modules to the core product team to influence the future direction of the AI platform.

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Co-architect and co-build production AI agents with customer engineering teams; own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations; help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows; advise customers post-sale on architecture, best practices, and roadmap-level decisions; run technical demos, trainings, and workshops for developer audiences; surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers; occasionally contribute code upstream when it meaningfully improves customer outcomes.

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Co-architect and co-build production AI agents with customer engineering teams, own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations, help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows, advise customers post-sale on architecture, best practices, and roadmap-level decisions, run technical demos, trainings, and workshops for developer audiences, surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers, and occasionally contribute code upstream when it meaningfully improves customer outcomes.

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The Forward Deployed Engineer is responsible for working closely with customers from onboarding through ongoing usage to help integrate and optimize AI solutions. They build new features, MVPs, and scalable solutions that impact customer outcomes, utilizing full-stack development skills in React, TypeScript, Node.js, and Python. They design, implement, and iterate on AI/ML applications including LLM prompting, tuning of voices, and transcribers. The role includes managing APIs and integrations with third-party systems to ensure seamless customer functionality. Collaboration with Product, Engineering, and Customer Success teams is essential to deliver tailored solutions. The engineer is expected to continuously iterate and improve AI solutions based on customer feedback and evolving requirements, while prioritizing and managing multiple projects under tight deadlines and maintaining high-quality results.

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Build and ship features for North, Cohere's AI workspace platform; develop autonomous agents that interact with sensitive enterprise data; experiment rapidly and with high quality to engage customers and deliver solutions that exceed expectations; work across the entire product lifecycle from conceptualization to production; lead end-to-end deployment of North in private cloud and on-premises environments, including planning, configuration, testing, and rollout.

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Design and develop AI applications primarily in Python. Run evaluations to validate models and package solutions for Kubernetes, AWS, or adapt them to customer on-premises clusters. Lead discovery sessions with customers, guide pilot projects, and ensure successful deployments, collaborating mostly remotely with occasional on-site workshops. Monitor system performance and reliability, add to logging, billing, and auth services, and build internal tooling to automate repetitive tasks. Provide feedback on patterns, pain points, and reusable modules to the core product team to influence the future direction of the AI platform.

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Lead complex AI-driven deployments in production, owning technical delivery across multiple deployments from scoping high-impact Agentic AI use cases to stable production. Apply technical expertise and problem-solving skills to design solution architectures, develop decision logic, deploy production-grade Generative AI agents, and align with key customer stakeholders, ensuring an outstanding experience and rapid time to value. Scope work effectively, sequence delivery, proactively remove blockers, and make trade-offs between scope, speed, and quality for successful and timely project delivery. Partner with product management to convert customer needs into actionable insights that influence the product roadmap. Develop reusable resources, best practices, and tools to scale the forward deployed engineering function across the organization.

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As a Lead Forward Deployed Engineer, you lead complex AI-driven deployments in production, owning technical delivery across multiple deployments from scoping AI use cases to stable production. You apply technical expertise, problem-solving skills, and creativity to help organizations address challenges by designing solution architectures, developing decision logic, deploying production-grade Generative AI agents, and aligning with customer stakeholders while ensuring rapid value for customers. You scope work, sequence delivery, remove blockers, and make trade-offs between scope, speed, and quality to ensure project success. You partner with product management to translate customer needs into product insights influencing the product roadmap. You develop reusable resources, best practices, and tools to scale the engineering function and actively coach and mentor junior engineers.

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The Forward Deployed Engineer (FDE) is responsible for leading end-to-end deployments of OpenAI’s models inside semiconductor and chip design organizations. This includes designing and shipping production AI systems around models, owning integrations with RTL repositories, verification environments, simulators, and internal tooling. The role entails leading discovery and scoping from pre-engagement through production rollout, translating ambiguous engineering pain points into hypothesis-driven use cases with measurable outcomes. The engineer will deliver AI-powered verification workflows such as change-aware test selection, directed test generation, and intelligent regression triage, moving them from prototype to daily production use. They will build systems that operate over large, evolving codebases and artifacts (RTL, tests, logs, waveforms, traces) where performance, latency, and failure handling impact architecture. The FDE will define and run evaluation loops measuring model and system quality against workflow-specific benchmarks. They are responsible for managing delivery state across multiple workstreams, balancing scope, speed, and robustness to protect production impact. The role requires distilling deployment learnings into hardened primitives, reference implementations, playbooks, and tooling reusable across customers, as well as surfacing field insights to inform model behavior, tooling gaps, and future product direction across the semiconductor stack.

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Advance inference efficiency end-to-end by designing and prototyping algorithms, architectures, and scheduling strategies for low-latency, high-throughput inference. Implement and maintain changes in high-performance inference engines, including kernel backends, speculative decoding, and quantization. Profile and optimize performance across GPU, networking, and memory layers to improve latency, throughput, and cost. Design and operate RL and post-training pipelines, jointly optimizing algorithms and systems to make inference and post-training workloads more efficient. Train, evaluate, and iterate on frontier models using these pipelines. Co-design algorithms and infrastructure for tightly coupled objectives, rollout collection, and evaluation to efficient inference. Identify bottlenecks across training engine, inference engine, data pipeline, and user-facing layers. Run ablations and scale-up experiments to understand trade-offs between model quality, latency, throughput, and cost, feeding insights back into model, RL, and system design. Profile, debug, and optimize inference and post-training services under real production workloads. Drive roadmap items requiring engine modification such as changing kernels, memory layouts, scheduling logic, and APIs. Establish metrics, benchmarks, and experimentation frameworks to rigorously validate improvements. Provide technical leadership by setting technical direction for cross-team efforts at the intersection of inference, RL, and post-training, and mentoring other engineers and researchers on full-stack ML systems work and performance engineering.