Search Results for: SRE

The **Senior Responsible AI Scientist** is a senior individual contributor who designs, validates, and operationalizes responsible AI (RAI) practices for machine learning systems, ensuring models are **safe, fair, privacy-preserving, transparent, and accountable** across their lifecycle. The role combines applied science depth with product and engineering pragmatism to make RAI measurable, repeatable, and scalable in real production environments.

The Senior NLP Scientist designs, trains, evaluates, and operationalizes Natural Language Processing (NLP) and Large Language Model (LLM) solutions that power product experiences and internal platforms in a software or IT organization. This role bridges state-of-the-art language modeling research with production-grade engineering, delivering measurable improvements in accuracy, safety, latency, and cost across language-driven workflows.

The **Senior Computer Vision Scientist** designs, trains, evaluates, and deploys computer vision and multimodal machine learning models that solve product and platform problems in a software or IT organization. This role blends research-grade rigor with production engineering discipline to deliver measurable improvements in accuracy, latency, robustness, and responsible AI compliance for vision-enabled experiences and services.

A **Senior Applied Scientist** designs, prototypes, validates, and productionizes machine learning (ML) and AI solutions that directly improve product capabilities and business outcomes. This role sits at the intersection of research-quality modeling and real-world software delivery—turning ambiguous problems into measurable improvements through data, experimentation, and robust engineering practices.

The **Senior AI Safety Researcher** is a senior individual-contributor scientist responsible for **identifying, measuring, and reducing safety risks** in machine learning systems—especially large language models (LLMs) and other foundation-model-powered capabilities—before and after they ship to customers. The role combines **research rigor** with **engineering pragmatism**, translating safety theory into concrete evaluations, mitigations, and decision-quality evidence for product teams.

The Responsible AI Scientist designs, evaluates, and improves AI/ML systems so they are safe, fair, reliable, privacy-preserving, and aligned with company policy and evolving external expectations. This role partners with applied science and engineering teams to build measurable responsible AI (RAI) requirements into model development and product release processes, translating abstract risk principles into concrete tests, mitigations, and launch gates.

The **Principal Robotics Research Scientist** is a senior individual-contributor research leader responsible for inventing, validating, and transferring state-of-the-art robotics and embodied AI capabilities into production-grade software and platforms. This role defines research direction, leads high-impact technical programs, and turns novel algorithms into reliable, measurable improvements in real-world robot performance.

The Principal Machine Learning Scientist is a senior individual contributor (IC) who sets technical direction for machine learning (ML) and applied research efforts, turning ambiguous business and product opportunities into scalable, measurable ML capabilities. This role leads end-to-end model strategy—from problem framing and experimental design through production evaluation, monitoring, and iteration—while ensuring quality, reliability, and responsible AI practices.

The **Principal Applied Scientist** is a senior individual-contributor (IC) research-and-engineering leader who designs, proves, and scales machine learning (ML) and AI capabilities that materially improve product performance, reliability, safety, and customer outcomes. This role sits at the intersection of scientific rigor and production engineering, translating ambiguous business opportunities into measurable ML-driven impact, and ensuring solutions can be deployed, monitored, governed, and improved over time.

The **Principal AI Safety Researcher** is a senior individual-contributor scientist who sets technical direction and delivers high-impact research that measurably reduces safety risks in deployed AI systems—especially large language models (LLMs), multimodal foundation models, and agentic systems. The role blends rigorous research with product-facing execution: inventing and validating new safety methods, translating them into evaluation and mitigation capabilities, and shaping how the organization ships AI responsibly at scale.

The **Machine Learning Scientist** is an individual contributor (IC) role in the **Scientist** job family within the **AI & ML** department, responsible for designing, validating, and improving machine learning approaches that solve measurable product and platform problems. This role translates ambiguous business or user needs into rigorous modeling hypotheses, experiments, and model artifacts that can be productionized with ML engineering and platform teams.

The **Lead Responsible AI Scientist** is a senior individual-contributor scientist who designs, validates, and operationalizes responsible AI practices across the AI/ML lifecycle—spanning data, model development, evaluation, deployment, and monitoring. The role ensures AI systems are **fair, explainable, safe, privacy-preserving, secure, and compliant** while still delivering measurable product and business value.

The **Lead NLP Scientist** is a senior applied research and product-facing science role responsible for designing, validating, and operationalizing Natural Language Processing (NLP) and Large Language Model (LLM) capabilities that power customer-facing software features and internal AI platforms. The role blends hands-on model development with technical leadership—setting scientific direction, raising engineering rigor in experimentation, and ensuring that NLP solutions meet product, reliability, privacy, and responsible AI expectations.

The **Lead Computer Vision Scientist** is a senior applied research and product-facing science role responsible for designing, developing, and scaling computer vision (CV) and multimodal machine learning capabilities into production-grade software. The role bridges state-of-the-art vision research with enterprise engineering practices—delivering measurable improvements in accuracy, latency, reliability, and cost across customer-facing and internal AI features.

The **Lead Applied Scientist** is a senior individual contributor (IC) who designs, proves, and productionizes machine learning (ML) and applied AI solutions that materially improve product capabilities and business outcomes. The role bridges research-quality methods and real-world software constraints—turning ambiguous problem statements into deployable models, measurable product impact, and reliable ML operations.