Senior AI Engineer
Design and maintain an enterprise AI platform focusing on LLM routing, agentic workflows, and RAG components. Build scalable deployment pipelines using Kubernetes and ArgoCD to empower developers to deploy AI-driven solutions.
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Design and maintain an enterprise AI platform focusing on LLM routing, agentic workflows, and RAG components. Build scalable deployment pipelines using Kubernetes and ArgoCD to empower developers to deploy AI-driven solutions.
Research and implement state-of-the-art Gen AI and ML solutions to solve complex National Security problems for customers. Translate real-world challenges into reusable solution recipes and platform capabilities to evolve Snorkel's AI tooling.
Lead the technical design and implementation of autonomous AI agents using Agentforce to drive business transformation for customers. This includes analyzing current business processes, designing hybrid reasoning models, and establishing an Agent Center of Excellence for organizational governance.
Build AI-native systems and agents to automate financial workflows and improve product intelligence for a Neobank app. Design and implement reliable, scalable AI tools focusing on accuracy, latency, and security in a production environment.
Build the core intelligence layer by training and fine-tuning open-source models using proprietary clinical data. Develop rigorous evaluation pipelines and scalable inference infrastructure to power LLM-driven workflows for clinicians.
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Define and evolve the technical architecture for Caseware's AI platform, focusing on scalable and secure AI capabilities. Design and operate AI systems using LLMs, agentic workflows, and governance frameworks to solve customer problems.
Lead end-to-end solution selling motions to drive adoption of the Atlassian Teamwork Collection across named enterprise accounts. Collaborate cross-functionally with GTM teams and provide customer insights to engineering to shape the product roadmap.
Lead the strategy and implementation of AI-powered customer support tools and automation to improve operational efficiency and self-service adoption. Manage the Zendesk platform and the evolution of internal and external knowledge bases to ensure accurate and accessible content.
Build an AI-native operator command center for restaurants to automate operational workflows and drive business outcomes. Own the foundations for orders, menus, and integrations to ensure a trustworthy and scalable product experience.
Record short video and audio clips using various tones and emotional styles to help AI understand human intent. Additionally, transcribe audio and video clips into text.
The role involves creating authoritative content on frontier AI and LLMs to build a category narrative for the insurance industry. Responsibilities include writing thought leadership, web copy, and ghostwriting for the CEO to make complex technical capabilities legible to senior executives.
Lead the design and deployment of enterprise-scale AI and data solutions, focusing on agentic AI workflows and scalable data pipelines. Collaborate with cross-functional stakeholders to ensure solutions meet business objectives and financial industry compliance standards.
Own the reliability and quality of an AI copilot for a trading platform by designing evaluation systems and benchmarks. Develop model improvement loops and monitoring processes to ensure safety and correctness in trading workflows.
Build a high-performance AI platform and backend in Rust to power an AI-native trading experience. Design robust APIs and safe action infrastructure for trading workflows, including orchestration of models and tools.
The CTO will define the long-term technical architecture for AI systems and infrastructure while building a global engineering organization from the ground up. They will lead technical execution, establish engineering standards, and align workstreams across product and research teams.
The role involves defining end-to-end AI system requirements and translating model capabilities into product decisions. The manager will collaborate with engineers to balance quality, latency, and cost while owning the overall product quality and user trust.
Lead the engineering strategy and execution to transform Machine Learning capabilities into reliable user experiences across backend, mobile, and desktop platforms. Manage a senior applications team while setting the architectural direction for AI-powered workflows and APIs.
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The VP of Research will lead the intelligence direction of the system, defining how AI reasons, evaluates, and improves. They will oversee model architecture decisions, evaluation frameworks, and the overall alignment and safety strategy.
Build end-to-end product features and design agent workflows that handle planning, tool use, and recovery. Integrate LLMs and external tools to create reliable, production-grade AI systems with low latency.
Build and improve core ML components across data, training, evaluation, and inference for real production systems. Debug model issues and iterate on features based on real-world user feedback and production constraints.
Architect and build large-scale ML systems covering training, inference, and deployment. Set technical standards for ML infrastructure and optimize GPU performance for production-grade systems.
The role involves translating research into scalable, production-grade ML systems and owning the end-to-end execution of data pipelines and inference architecture. You will be responsible for fine-tuning models and optimizing GPU performance to ensure reliability and low latency in real-world products.
The role involves building and owning critical machine learning subsystems from data preparation to production deployment. You will collaborate cross-functionally to turn research ideas into scalable, reliable AI products while mentoring other engineers.
The Software Engineer will build and maintain cross-platform desktop applications using Electron, focusing on reliability and real-time performance. Responsibilities include integrating AI-powered features and optimizing system behavior across macOS and Windows environments.
Own the end-to-end execution of ML systems, translating research into scalable production-grade solutions. Responsible for data pipelines, model fine-tuning, and optimizing inference systems for latency and cost.
Design and implement complex multi-file coding tasks including bug fixes, feature development, and testing. Review peer-generated tasks for accuracy and create high-quality reference implementations.
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Design and implement complex multi-file coding tasks including bug fixes, feature development, and testing. Review peer-generated tasks for accuracy and create high-quality reference implementations.
Design and implement complex multi-file coding tasks including bug fixes, feature development, and testing. Review peer-generated tasks for accuracy and create clear natural-language specifications and reference implementations.
Design and implement complex multi-file coding tasks including bug fixes, feature development, and test suites. Review peer-generated tasks for correctness and ensure alignment between specifications and code outputs.
Design, deploy, and monitor end-to-end Machine Learning and Generative AI solutions. Process data sources to extract business insights and build prototypes using cutting-edge tools.
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