Integrate and evaluate foundation models from multiple providers and determine the appropriate model, prompting, routing, and execution strategy for different tasks.
Develop structured-output, function-calling, and multi-step reasoning workflows for enterprise use cases.
Build evaluation frameworks to measure task completion, accuracy, reliability, latency, and cost.
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Record-keeping: Maintain detailed records of quality tests, inspections, and audits. Generating reports on quality performance and communicating quality issues to relevant stakeholders.
Continuous Improvement: Identify areas of improvement from assembly procedures to tooling, to enhance product quality, manufacturing efficiency, and reduce defects
Training: Provide training to staff on quality standards, procedures, and best practices to ensure that quality requirements are consistently met
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We're looking for a FinOps Engineer to help drive cost optimization and cloud financial governance across our AWS infrastructure and GCP footprint. You'll work cross-functionally with Engineering, Finance, and Product to find and ship savings, build the tooling necessary to maximize efficiency of every dollar spent in the cloud, and cultivate a culture of cost awareness across engineering teams.
This is a hands-on engineering role, not an analyst seat. You'll do the schlep work other teams don't have time for, digging into a bucket's access pattern, writing the Terraform or Lambda that ships the fix, working on a product feature chasing a number until it reconciles, and influencing product decisions when cost to reliability tradeoffs hit. Strong communication matters as much as technical depth, you'll regularly explain a savings case to an engineering team, a finance stakeholder, and a leadership audience in the same week.
Own the enterprise collaboration platform, including IM, meetings, calendar, and desktop and mobile clients, with a focus on reliability, performance, and end-to-end security
Lead the productivity tool suite (documents, secure mail, tasks, workflow, and forms), driving integration across tools and embedding AI features such as summarization and meeting transcription where they add real value
Build and grow an open platform and APIs that enable internal teams, partners, and bots to integrate with and extend the workspace
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Relevant AI Projects: CV must clearly highlight relevant AI projects, including the candidate's role, technical contributions, technologies, and impact
Strong understanding of the core foundations and underlying technologies of AI.
Strong preference with startup experience and the ability to thrive in fast-paced environments.
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Real-world Feedback Loops: Leverage multi-channel user feedback and real-world task data as primary research signals; design experiments and datasets to continuously improve agent and retrieval performance in production scenarios
2-8+ Year hands-on experience with LLM, RAG and AI agent systems in production
Explore and integrate AI capabilities into mobile applications, including on-device AI inference, LLMs, intelligent agents, and AI-powered user experiences.
Design and optimize AI-related components for mobile and edge devices, with a focus on inference latency, memory usage, CPU/GPU utilization, power efficiency, and overall user experience.
Collaborate with cross-functional teams to define, design, and deliver new features across Android, iOS, and shared C++ infrastructure.
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