Docker AI Jobs

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Ship critical infrastructure managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding the company's value to customers, pushing back on requirements if a better, faster solution is seen. Demonstrate full-stack proficiency by working across system boundaries, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate so focus can be on quality, architecture, and product taste. Constantly raise the velocity bar by optimizing development loops, refactoring legacy patterns, automating workflows, and fixing broken processes.

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As a Forward Deployed Engineer Lead, you will own the end-to-end technical strategy, execution, and delivery of complex agentic applications, from early pre-sales discovery through production deployment. Responsibilities include partnering with Deployment Strategists and Sales to understand enterprise customer needs, architecting solutions, and developing transformative agentic applications. You will architect and build complex agentic systems using state-of-the-art models, orchestrate sophisticated LLM workflows, and integrate deeply with enterprise infrastructure. Collaboration with research teams to adapt and fine-tune models for customer-specific needs and contributing to the internal codebase for inference, fine-tuning, and evaluation is required. You will own end-to-end deployments across hybrid environments including public cloud, VPC, and on-premises, ensuring production-grade scalability, performance, and reliability. Additionally, you will shape and scale the Forward Deployed Engineering organization by defining playbooks, best practices, technical standards, and providing mentorship to support team growth.

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As a Forward Deployed Engineer at Reflection, you will partner with Deployment Strategists and Sales to understand enterprise customer needs, architect solutions, and develop transformative agentic applications. You will build agentic systems using state-of-the-art models, orchestrate LLM workflows, integrate with enterprise infrastructure, and deploy reliable production systems. You will collaborate with research teams to adapt and fine-tune models for customer-specific needs. You will support end-to-end deployments across hybrid environments such as public cloud, VPC, and on-premises, ensuring scalability, performance, and reliability in production. Additionally, you will contribute to evolving playbooks, processes, and best practices as part of the growing Forward Deployed Engineering organization.

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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will own the production outcome, taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will ensure full-stack integrity by overseeing the health of the platform, ensuring seamless integration between the AI core and all full-stack components from APIs to UI. Additionally, you will build automated systems to monitor model performance and data drift across geographically dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead the response for production issues in mission-critical environments, translate deep technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to ensure field lessons influence future technical architecture and decisions.

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As a backend engineer, you would play a critical role in the search architecture at Exa. Your work may involve building massive-scale machine learning systems, working on projects based on your skills and interests, such as recreating Google-level keyword search over 10 billion pages in one month, building state-of-the-art crawling systems that work optimally for any website, and building custom vector databases that can run over a billion vectors in under 100 milliseconds.

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Lead efforts to drive the design and development of customer-facing multi-modal machine learning inference systems. Work with the Platform and Inference teams on building inference systems for the next generation of models, focusing on optimization, model tuning, and deployment. Partner with leading cloud providers to deliver hosted Stability AI inference solutions. Serve as a strategic thought partner for leaders across the organization on driving business impact through machine learning. Contribute to bringing new Stability models and pipelines into existence. Prototype and productionize inference platform improvements and new features.

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As an Agentic AI/ML Engineer Intern, you will design and implement agentic workflows with tool use, memory, and orchestration to automate repetitive tasks and answer questions over internal and customer-facing data. You will contribute to AI Ops infrastructure including orchestration, evaluations, and observability, enabling agent-native DevOps to automate engineering and internal operations workflows. You will build and optimize RAG pipelines with vector databases and knowledge graphs to ground agents in the correct context. Additionally, you will set up evaluation pipelines to measure agent quality, reliability, and performance. This role involves prototyping, evaluating, and shipping agent-native solutions to multiply the impact of teams and technology, supporting scaling of customer base and operations without scaling headcount linearly.

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Design and build agentic workflows that leverage tool use, memory, planning, and orchestration to automate repetitive tasks and enable natural-language access to internal and customer-facing data. Contribute to FieldAI's AI Ops platform by developing agent infrastructure for orchestration, evaluation, observability, and reliability, and apply these capabilities to create agent-native DevOps workflows that automate engineering, support, and operational processes. Develop and optimize retrieval systems, including RAG pipelines, vector databases, and knowledge graph integrations, to provide agents with accurate, relevant, and scalable context. Build evaluation frameworks and automated testing pipelines to measure agent quality, reliability, safety, latency, and business impact, using those insights to continuously improve system performance. Prototype, iterate, and deploy AI-powered tools that improve internal productivity and deliver actionable insights to customers. Partner closely with engineering, product, field operations, and customer-facing teams to identify high-leverage opportunities for automation and agent-driven workflows.

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Research and develop latest TTS models based on LLM and Flow Matching; develop and advance emotion controllable TTS models; build and improve quality of speech synthesis data using latest generative models; develop and apply multilingual and multi-speaker TTS models to services; optimize TTS models for server and on-device environments; develop real-time (streaming) speech synthesis systems and optimize latency; improve inference and training pipelines to enhance speech generation quality.

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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications while supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will oversee the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components from APIs to UI, maintaining a responsive and production-ready environment. You will build automated systems to monitor model performance and data drift across geographically dispersed environments to ensure reliability. You will manage the technical lifecycle within diverse regulatory frameworks and lead the response for production issues in mission-critical environments, ensuring rapid resolution and building guardrails to prevent recurrence. You will translate deep technical performance metrics into clear insights for senior international government officials and partner with Engineering and ML teams to ensure lessons learned influence future technical architecture and decisions.