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).