Applied AI Engineer Jobs

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As an AI Engineer (New Graduate) at Distyl, you will design, implement, and deploy GenAI applications under the guidance of senior engineers, contributing to prompt design, agent logic, retrieval-augmented generation (RAG), and model evaluation to build full-stack AI applications that deliver measurable business value. You will gain exposure to customer-facing work by shadowing technical conversations and learning how business needs are translated into system design, with opportunities to take on more responsibility in technical decisions and implementation. You will partner with senior engineers to understand customer problems and translate requirements into technical solutions, participate in customer discussions, solution design sessions, and iterative delivery. Additionally, you will help improve Distillery, Distyl’s internal LLM application platform, by building reusable components, tools, and workflows and learn best practices for scalable, maintainable AI infrastructure. You will write clean, well-tested, observable production-quality code that meets reliability, performance, and security standards and learn how production AI systems are monitored, debugged, and improved over time. You will assist with evaluating AI systems across accuracy, latency, cost, and robustness, applying user feedback and metrics to improve system performance. Finally, you will continuously develop your skills in LLMs, software engineering, and AI through mentorship, code reviews, and hands-on project work, learning modern development workflows and deployment practices used in enterprise AI.

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As a Software Engineer working on AI systems, responsibilities include playing a foundational role in research, experimentation, and rapid improvement of AI systems to build a capable, reliable AI automation platform used worldwide in mission critical production environments. Tasks involve designing experiments and testing ideas to optimize key internal AI benchmarks, designing and improving evaluation frameworks to accelerate experimentation speed and direction, training, fine-tuning, and optimizing machine learning models, performing rigorous evaluation and testing for model accuracy, generalization, and performance, collaborating and contributing to core product development to enhance platform capabilities, and setting up observability and monitoring systems to safety check model behavior in critical settings.

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Assist in designing and implementing custom large language models (LLMs) and fine-tuning models for specific tasks. Build and experiment with agent libraries and workflow orchestration frameworks. Explore neo-cloud technologies, containerized environments, and virtualized infrastructure. Learn and apply security and privacy best practices in AI pipelines and deployments. Collaborate with the team to document, test, and optimize agent behaviors and models. Participate in knowledge sharing and mentorship sessions to gain exposure to AI, cloud, and security tradecraft.

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Design and ship production systems around models, owning integrations, data flows, reliability, and on-call readiness. Lead discovery and scoping from pre-sales through post-sales, including problem framing, constraints, trade-offs, and a delivery plan. Define launch criteria for regulated contexts, outcome metrics, and drive adoption until production impact is proven. Build in sensitive data environments where auditability, validation, and access controls drive architecture decisions. Run evaluation loops that measure model and system quality in life science workflows to drive model and product improvements. Distill production learnings into hardened primitives, reference architectures, and templated workflows that scale across regulated life sciences environments.

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As a Forward Deployed Engineer, you will be the driving force behind customer deployments, taking AI solutions from early concept and pilot to production launch with enterprise-grade reliability. You will translate cutting-edge AI capabilities into practical, high-performance systems tailored to real-world customer needs, designing and implementing agentic voice AI solutions that integrate seamlessly into customer workflows and infrastructure. Your role involves prototyping, iterating, and deploying AI-driven systems in close collaboration with enterprise customers, working closely with customers to define success criteria and ensure they achieve meaningful outcomes on Cartesia's platform. You will have significant autonomy to shape customer solutions and directly impact how cutting-edge AI is deployed at scale across global organizations.

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Design and implement AI agents and extend existing agents with new capabilities including managing agent context using techniques like sub-agents and retrieval-based context management. Develop complex tools for agents such as computer use and browser use. Research and develop multi-agent orchestration and tool calling systems to enable collaboration between agents. Build and maintain production-grade APIs and AI-powered features across backend services and user-facing experiences. Evaluate AI performance through tests and evaluations and iterate on prompts, agent tools, and orchestration to improve output quality and reliability.

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Develop AI agents and multi-agent systems by designing and implementing agents and extending existing agent capabilities, including managing the agent's context using techniques like sub-agents and retrieval-based context management. Develop complex tools for agents such as computer use and browser use. Research and build systems for multi-agent orchestration and agent tool calling to manage collaboration and tool usage. Engage in full-stack development to build and maintain production-grade APIs and AI-powered features, covering backend services to user-facing experiences. Evaluate AI performance using tests and evaluations to iterate and improve prompts, agent tools, and orchestration for better output quality and reliability.

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Build and ship end-to-end AI-powered products and systems that meaningfully improve creative workflows while balancing quality, performance, and reliability. Integrate state-of-the-art models, combining internal research and external capabilities to unlock new product experiences. Measure and improve system quality in production using experimentation, evaluation frameworks, and real-world feedback to guide iteration. Design the systems that support intelligent behavior, including how models gather context, make decisions, and operate efficiently at scale.

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As a Research Intern at Snorkel AI, you will contribute to internal research and academic collaborations by exploring and validating new ideas that may influence future publications, open-source artifacts, and long-term product directions. Responsibilities include developing and evaluating new methods for data development for foundation models and enterprise AI systems such as dataset construction, augmentation, synthetic data, and evaluation; researching supervision and evaluation techniques like rubrics and verifiable rewards; designing experiments and conducting rigorous empirical studies including ablations, benchmarks, and error analyses; building lightweight research prototypes and tooling in Python to support internal studies; and collaborating with academic partners and internal research teams by reading papers, proposing hypotheses, and iterating quickly.

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At TrustLab, the AI Engineer will train, evaluate, and monitor new and improved LLMs and other algorithmic models. They will test and deploy content moderation models in production and iterate based on real-world performance metrics and feedback loops. The engineer will ensure results are delivered to customers, push for changes in approach when needed, and proactively execute cross-functional tasks. Their work includes developing, tuning, and optimizing LLM-driven solutions that interpret and reason about complex digital content, collaborating closely with engineers, researchers, and product leaders from R&D through production launches to shape AI-powered content experiences for millions of users.