Applied AI Engineer Jobs

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Implement and integrate AI functionality into key product features, craft and iterate on prompts to improve LLM reliability and usefulness, build AI-powered flows that feel intuitive and responsive to developers, evaluate and test AI outputs to ensure performance and accuracy, work alongside engineers to deliver robust, production-grade code, stay current with LLM tools, APIs, and best practices, deliver reliable, high-quality AI-powered product experiences, translate product needs into technical AI implementations, tune and test prompts for real-world use cases and developer workflows, collaborate closely with engineers and researchers, and contribute across frontend, backend, and integration layers.

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Build LLM agents for document understanding, search, and image recognition that transition quickly from prototype to production; write clean, tested Python code that scales, focusing on working AI features rather than research papers; collaborate with AI and product teams to iterate on solutions that customers actually use; manage multiple projects with high ownership using LangGraph and cutting-edge AI frameworks; build agents, tune models, and ship AI solutions that power the entire platform.

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The AI Engineer will design and develop intelligent agents powered by large language models (LLMs) using tool calling, orchestration frameworks, and advanced context management to enable reasoning, planning, and autonomous decision-making across complex workflows. Responsibilities include working hands-on with modern agentic stacks such as LangGraph and Autogen, implementing asynchronous and streaming architectures, and ensuring production-grade observability to build scalable real-world AI systems.

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Drive the end-to-end technical deployment of Latent Labs models into customer environments, ensuring seamless integration with existing scientific and IT infrastructure. Design and build production-grade API integrations, data pipelines and model-serving infrastructure tailored to each customer’s requirements. Work on-site or embedded with pharma and biotech partners to scope technical requirements, troubleshoot issues and deliver solutions. Ensure deployments meet enterprise standards for security, performance and reliability. Serve as the technical point of contact for assigned customers, building trusted relationships with their scientific and engineering teams, including spending time working on-site at international partner locations as needed. Gather and synthesise customer feedback, translating it into actionable insights for product, research and platform teams. Collaborate with internal teams to shape the product roadmap based on real-world deployment learnings. Create technical documentation, integration guides and best-practice resources for customers. Stay on top of the latest developments in ML infrastructure, model serving and cloud-native tooling. Gain a strong working understanding of protein and cell biology as it relates to the product. Participate in knowledge sharing, including organizing and presenting at internal reading groups.

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Forward Deployed Engineers lead complex end-to-end deployments of frontier models in production alongside strategic customers, owning discovery, technical scoping, system design, build, and production rollout while partnering with customer engineering and domain teams. They own technical delivery across multiple deployments from prototype to stable production, build full-stack systems to deliver customer value, embed closely with customer teams to understand needs and guide adoption, scope work, sequence delivery, and remove blockers early. They make trade-offs between scope, speed, and quality, contribute directly in the code when needed, codify working patterns into reusable tools and playbooks, share field feedback to help Research and Product improve models, and keep teams moving through clarity and follow-through.

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The AI Factory Customer Engineer is responsible for translating business requirements into AI/ML model requirements, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms, particularly transformers, and leveraging existing algorithms from research. They test and evaluate the models, benchmark their quality, and publish models, datasets, and evaluations. This role includes deploying models in production through containerization, working with customers and internal teams to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.

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Partner with frontier AI research labs to design datasets and environments that improve model performance. Lead technical conversations with customer researchers to understand model capabilities, failure modes, data requirements, and success criteria. Probe model behavior through systematic evaluation to uncover weaknesses and identify high-impact data interventions. Design evaluation frameworks, calibration processes, and quality rubrics that establish measurable project success metrics. Develop technical specifications for data projects that balance research rigor with operational feasibility. Serve as a thought partner to customer research teams throughout the sales cycle, building trust and credibility. Stay current on frontier AI research, RL environment design, post-training techniques, and evaluation methodologies.

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The AI Workflow Engineer is responsible for identifying opportunities where AI can enhance the quality, speed, scale, and effectiveness of marketing and go-to-market programs, scoping ambiguous problems, prioritizing opportunities, and driving execution from prototype to production. They design and deploy workflows that combine agents, tools, and human review in production environments, rapidly prototype and ship using automation platforms, OpenAI's API platform, and Codex, and build and maintain a small set of centrally managed "golden workflows" that support critical marketing and lifecycle programs. Additionally, they develop reusable templates, tooling, and documentation that enable teams to safely self-serve over time, and help operators adopt frontier capabilities through hands-on training, pair programming, and direct collaboration.

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Partner with frontier AI research labs to design datasets and environments that improve model performance. Lead technical conversations with customer researchers to understand model capabilities, failure modes, data requirements, and success criteria. Probe model behavior through systematic evaluation to uncover weaknesses and identify high-impact data interventions. Design evaluation frameworks, calibration processes, and quality rubrics that establish measurable project success metrics. Develop technical specifications for data projects that balance research rigor with operational feasibility. Serve as thought partner to customer research teams throughout the sales cycle, building trust and credibility. Stay current on frontier AI research, RL environment design, post-training techniques, and evaluation methodologies.

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Design and build AI agents and AI-enhanced features iteratively that help customers debug, fix, and create Playwright and API tests faster. Implement solutions full stack with your team. Get in touch with users to learn from their feedback directly to build solutions that are delightful and solve real problems.