Applied AI Engineer Jobs

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The responsibilities include building agent-driven enrollment and parent communication pipelines that scale significantly without proportional headcount growth; creating and managing parallel simulations of students testing curriculum to identify gaps and generate improvements; developing automated culture and community agents for engagement, onboarding, and retention at machine scale; constructing real-time operational dashboards to provide leadership with visibility into various business aspects such as enrollment, academic progress, parent satisfaction, and campus operations; designing AI-first workflows for guides, advisors, and operational staff to reduce administrative burdens and refocus on students; building systems called Brainlifts to capture and compound institutional knowledge over time; and integrating these capabilities into Alpha's broader AI ecosystem including EPHOR, Alpha GPTs, and Fleet/Swarm infrastructure.

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Build end-to-end AI features by architecting and shipping fullstack solutions from React frontends to Python backend services that leverage voice AI and large language models to automate clinical workflows; implement and fine-tune audio processing pipelines ensuring accurate performance of Automatic Speech Recognition (ASR) and LLM agents in diverse medical environments; translate complex clinical feedback into technical solutions by rapidly prototyping and deploying improvements to model behavior, prompting strategies, and audio handling; optimize fullstack performance for real-time audio streaming and token generation to minimize latency for seamless clinician interaction; partner with implementation and clinical teams to shorten the feedback loop by shipping critical integrations and feature requests from concept to production quickly.

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As an Applied AI Engineer at Snorkel, you will research and utilize state-of-the-art generative AI and machine learning techniques to deliver solutions to customers. Responsibilities include partnering with customers from use case scoping and data exploration to model development and deployment, using Snorkel Flow or custom approaches to provide real business value. You will develop and implement AI systems such as retrieval-augmented generation, fine-tuning pipelines, prompt engineering recipes, and agentic workflows. The role involves creating augmented datasets and evaluation workflows to ensure model reliability and transparency, managing relationships with customer leadership and stakeholders, and collaborating with pre-sales Solutions and Product teams to align customer needs with platform capabilities. You will work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices, lead stakeholder education on AI capabilities, represent customer feedback to product teams, and conduct enablement workshops for customers. The position requires up to 25% annual travel.

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Build production AI systems by designing, developing, and deploying robust AI applications using LLMs, including prompt engineering, agent workflows, tool use, and full-stack AI products. Work directly with customers by partnering closely with enterprise stakeholders to understand complex problems and translate them into impactful AI solutions. Lead system architecture by designing scalable architectures for production AI systems that balance performance, reliability, cost, and maintainability. Develop internal platform infrastructure by contributing to Distillery, the internal LLM application platform, building reusable infrastructure, tools, and workflows used across customer deployments. Evaluate AI systems rigorously by developing evaluation frameworks that measure model performance across accuracy, latency, cost, reliability, and safety. Ship production-grade systems ensuring they meet high standards for observability, reliability, security, and maintainability. Raise the engineering bar by improving development workflows, evaluation practices, and deployment strategies as the AI platform evolves.

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As a Forward Deployed AI Engineer at Lorikeet, you will act as an AI expert and evangelist for the customer base, leading AI implementation projects from kickoff to successful deployment. Your responsibilities include building and configuring workflows in Lorikeet's AI tools to meet customers' unique goals, understanding customers' businesses and recommending innovative AI solutions beyond typical customer support, working with the product team to represent customer needs and provide feedback, guiding customers through technical implementation while building strong relationships, and solving complex integration challenges creatively and efficiently.

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Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment. Develop and implement state of the art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes, and agentic workflows. Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. Forge and manage relationships with customers' leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow. Collaborate closely with pre-sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup. Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices. Lead stakeholder education on quantitative capabilities, helping them to understand the strengths and weaknesses of different approaches and what problems are best-suited for Snorkel AI. Serve as the voice of customers for new AI paradigms, data science workflows, and share customer feedback to product teams. Conduct one-to-few and one-to-many enablement workshops to transfer knowledge to customers considering or already using Snorkel AI. Travel up to 25% annually.

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Partner with customers to build and deploy impactful Gen AI and machine learning solutions, from use case scoping and data exploration to model development and deployment, leveraging Snorkel Flow or designing custom approaches. Develop and implement state-of-the-art AI systems such as retrieval-augmented generation (RAG), fine-tuning pipelines, prompt engineering recipes, and agentic workflows. Create augmented real-world datasets and comprehensive evaluation workflows to ensure model reliability, transparency, and stakeholder trust. Manage relationships with customers' leadership and stakeholders to ensure successful development and deployment of AI projects with Snorkel Flow. Collaborate closely with pre-sales Solutions and Product teams to map customer needs to existing capabilities, prioritize roadmap gaps, and guide successful project setup. Work with other Applied AI Engineers to standardize solutions and contribute to internal tooling and best practices. Lead stakeholder education on quantitative capabilities, helping them understand the strengths and weaknesses of different approaches and suitable problems for Snorkel AI. Serve as the voice of customers for new AI paradigms and data science workflows, sharing customer feedback with product teams. Conduct enablement workshops to transfer knowledge to customers considering or using Snorkel AI. Travel up to 25% annually.

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Develop, test, and deploy production-level code across backend and AI systems. Collaborate with AI researchers to integrate and optimize large language models for insurance workflows. Build data processing and evaluation pipelines for unstructured document inputs such as PDFs, emails, and images. Contribute to core infrastructure including APIs and orchestration logic powering the AI Workspace for Insurance. Work cross-functionally with product and customer teams to identify and solve real business problems using AI. Participate in design reviews, code reviews, and rapid iteration cycles.

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Transform foundational research ideas into scalable algorithms and prototypes for structured AI systems. Build evaluation harnesses, datasets, and benchmarks to measure real signal from research ideas and define metrics to quantify progress. Develop efficient learning methods for relational, tabular, graph, and enterprise datasets, including representation learning architectures and compression-aware models. Implement fast training and inference pipelines using ML frameworks like PyTorch, JAX, or custom kernels, optimizing memory usage, compute utilization, and data movement to improve cost, latency, and throughput for large-scale ML workloads. Design hybrid AI systems integrating symbolic, relational, and neural components enabling AI models to reason over structured datasets without relying on text intermediaries. Collaborate with Research Scientists, Systems Engineers, and Product Engineering teams to validate hypotheses, integrate algorithms into platforms, and ship features for enterprise workloads. Conduct controlled experiments with clear benchmarks and reproducible evaluations, driving the cycle from prototype through production to optimization.

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Run projects together with customers' engineering teams, analyze and process engineering data using their platform and python libraries, develop tailored solutions and workflows, apply machine- and deep-learning workflows to different engineering/physics problems, deliver proofs-of-concept demonstrating the value of their technology in CAD, CAE, and manufacturing, train customer teams to effectively use their methods and platform ensuring smooth AI adoption into their workflows, and work closely with developers to translate customer needs and feedback into product improvements.