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 **Senior AI Research Scientist** is a senior individual contributor who leads the conception, execution, and translation of advanced machine learning research into scalable capabilities for software products and platforms. The role combines scientific depth (novel algorithms, rigorous experimentation, publication-quality results) with engineering pragmatism (reproducibility, efficient training, model evaluation, and transfer to production or applied 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.

A **Research Scientist** in an AI & ML department advances the company’s machine learning capabilities by inventing, validating, and transferring new modeling approaches into production-ready pathways. The role balances scientific rigor (hypothesis-driven research, reproducibility, peer-quality writing) with practical engineering awareness (data realities, latency/cost constraints, deployment considerations).

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 Responsible AI Scientist** is a senior individual contributor who ensures AI/ML systems are **trustworthy, safe, fair, transparent, privacy-preserving, and compliant** from research through production operations. The role exists to translate responsible AI principles and external expectations (regulatory, customer, ethical, and brand trust) into **practical technical requirements, measurable controls, and repeatable engineering patterns** across AI products.

The **Principal Research Scientist** is a senior individual-contributor (IC) research leader in the **AI & ML** organization of a software or IT company. The role exists to **create differentiated, production-relevant AI innovations**—advancing the state of the art while translating research into capabilities that improve product quality, platform performance, customer outcomes, and business growth.

The **Principal NLP Scientist** is a senior individual-contributor (IC) scientific leader responsible for advancing state-of-the-art and state-of-practice Natural Language Processing (NLP) capabilities into reliable, secure, and measurable product outcomes. This role designs and validates NLP/LLM approaches, sets technical direction across multiple teams, and ensures models meet enterprise standards for quality, safety, privacy, and operational excellence.

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 Computer Vision Scientist is a senior individual contributor who shapes and delivers computer vision (CV) and multimodal machine learning capabilities that materially impact product outcomes, platform reliability, and competitive differentiation. This role owns end-to-end scientific leadership from problem framing and dataset strategy through model development, evaluation, deployment, and continuous improvement in production environments.

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 **Principal AI Research Scientist** is a senior individual-contributor research leader responsible for inventing, validating, and transferring state-of-the-art AI/ML methods into product-grade capabilities for a software or IT organization. This role combines deep technical research rigor with practical engineering judgment to ensure innovations are not only novel but also deployable, safe, and measurable in real-world systems.

The **NLP Scientist** designs, trains, evaluates, and improves natural language processing (NLP) models that power user-facing product experiences and internal AI capabilities (e.g., search, chat, summarization, classification, information extraction, and enterprise knowledge assistants). The role blends applied research rigor with production-minded engineering to deliver measurable improvements in language understanding and generation systems.

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 Robotics Research Scientist** is a senior technical leader responsible for inventing, validating, and transitioning robotics and autonomy algorithms into production-grade software capabilities. The role combines applied research rigor (hypothesis-driven experimentation, benchmarking, publication/patent-quality documentation) with pragmatic engineering judgment to deliver measurable improvements in robot performance, safety, reliability, and cost.

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 Research Scientist is a senior individual contributor (IC) responsible for defining, executing, and operationalizing applied research in AI/ML that measurably improves product capabilities, platform performance, or customer outcomes. This role bridges scientific rigor and real-world delivery: it turns ambiguous business problems into testable hypotheses, produces novel methods or model improvements, and guides production-grade implementation through close partnership with engineering and product teams.

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 Machine Learning Scientist is a senior individual-contributor (IC) scientific leader responsible for turning ambiguous product and business problems into measurable machine learning (ML) outcomes, and for guiding the design, development, validation, and iteration of ML models that operate reliably in production. This role blends deep applied ML expertise with technical leadership: setting scientific direction for a problem area, raising the technical bar across the team, and ensuring model quality, safety, and business impact.

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.

The Lead AI Safety Researcher is a senior individual contributor (IC) scientist who drives the research, validation, and deployment-readiness of safety approaches for machine learning and generative AI systems used in software products and enterprise platforms. The role focuses on preventing, detecting, and mitigating harmful model behaviors (e.g., hallucinations with high confidence, unsafe instruction-following, prompt injection susceptibility, privacy leakage, bias and unfair outcomes, and misuse enablement) while balancing product utility, latency, and cost.

The Lead AI Research Scientist is a senior, research-driven technical leader responsible for inventing, validating, and transferring state-of-the-art AI/ML methods into product-grade capabilities that materially improve business outcomes. The role combines deep scientific rigor (hypothesis-driven research, experimentation, peer-level technical judgment) with practical engineering sensibilities (reproducibility, scalability, reliability, and responsible deployment).

A **Computer Vision Scientist** designs, trains, evaluates, and iterates on computer vision models that convert images and video into reliable product capabilities (e.g., detection, segmentation, tracking, OCR, pose estimation, scene understanding). The role exists in software and IT organizations to transform visual data into scalable, maintainable ML services that create measurable customer and business outcomes.

The **Associate Robotics Research Scientist** designs, prototypes, and validates machine learning and algorithmic approaches that enable robots to perceive, plan, and act in the physical world. The role blends applied research with engineering rigor: turning ideas from papers, experiments, and simulations into measurable improvements in a robotics software stack.

The **Associate Responsible AI Scientist** supports the design, evaluation, and continuous improvement of machine learning (ML) and generative AI systems to ensure they are **fair, reliable, transparent, privacy-preserving, secure, and aligned with company policy and applicable regulation**. This is an early-career applied science role that combines **measurement (metrics and testing), technical analysis (data/model behaviors), and governance-ready documentation** to help teams ship AI features responsibly.

The Associate Research Scientist is an early-career research individual contributor (IC) within the AI & ML department who designs, executes, and communicates machine learning research that can be transferred into software products, developer platforms, or internal AI capabilities. The role blends scientific rigor (hypothesis-driven experimentation, statistical reasoning, reproducibility) with practical engineering habits (clean code, versioning, compute-aware experimentation) to produce validated improvements to models, methods, or evaluation frameworks.