AI & ML

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.

The **Associate NLP Scientist** is an early-career applied research and development role responsible for building, evaluating, and improving Natural Language Processing (NLP) models that power software features such as search, summarization, classification, conversational experiences, document understanding, and developer productivity tools. The role blends scientific rigor (hypothesis-driven experimentation, benchmarking, statistical thinking) with practical engineering (reproducible pipelines, model packaging, evaluation automation) under the guidance of more senior scientists and engineering leads.

The **Associate Machine Learning Scientist** is an early-career individual contributor who helps design, prototype, evaluate, and incrementally improve machine learning (ML) models that power product features, internal platforms, or analytics capabilities. The role focuses on **problem framing, experimentation, model development, and measurement**, with increasing responsibility for reproducible research and production-aware modeling practices.

The **Associate Computer Vision Scientist** is an early-career applied research and development role within an AI & ML organization, focused on building, evaluating, and improving computer vision models that power production software features. The role blends scientific rigor (experimentation, statistical thinking, paper-to-code translation) with engineering discipline (reproducibility, MLOps readiness, performance profiling) to deliver measurable product outcomes.

The **Associate Applied Scientist** is an early-career applied research and machine learning practitioner who translates business problems into measurable ML solutions, prototypes models, validates them through rigorous experimentation, and partners with engineering to deploy and monitor them in production. This role sits at the intersection of **scientific method** and **software delivery**, combining statistical rigor with practical constraints such as latency, cost, privacy, and reliability.

The **Associate AI Safety Researcher** supports the design, evaluation, and improvement of AI system safety in a software or IT organization, with a focus on reducing harmful outcomes and increasing trustworthy behavior in deployed models (especially large language models and related ML systems). The role blends empirical research, applied experimentation, and engineering-adjacent execution to translate safety hypotheses into measurable evaluations, mitigations, and production-ready guidance.

The **Associate AI Research Scientist** is an early-career research role responsible for designing, executing, and communicating machine learning research that advances model capability, efficiency, reliability, and responsible use—typically transitioning validated ideas into prototypes that can be integrated into products and platforms. The role blends scientific rigor (hypothesis-driven experimentation, statistical evaluation, and reproducibility) with practical engineering instincts (clean implementations, scalable training/evaluation pipelines, and clear handoffs to applied engineering teams).

The Applied Scientist is an individual contributor role within the AI & ML department responsible for designing, validating, and productionizing machine learning (ML) and statistical solutions that measurably improve software products and internal platforms. This role bridges research-quality modeling with real-world engineering constraints, translating ambiguous business problems into deployable, monitored, and continuously improved models.

The **AI Safety Researcher** is an individual-contributor scientist role responsible for identifying, measuring, and reducing safety risks in machine learning systems—especially large language models (LLMs) and other generative or decision-support models—through rigorous research, evaluation, and applied mitigation work. The role blends experimental research with practical engineering to ensure models behave reliably, resist misuse, and meet internal Responsible AI standards before and after deployment.

The **AI Research Scientist** is an individual contributor in the **Scientist** role family within the **AI & ML** department, responsible for advancing the organization’s machine learning capabilities through applied and/or foundational research, rapid experimentation, and measurable translation of research outcomes into product or platform improvements. The role blends scientific rigor (hypothesis-driven research, statistical validity, reproducibility) with software engineering pragmatism (prototyping, evaluation pipelines, and collaboration with engineering to land outcomes).