Search Results for "Jenkins"

The **Senior Robotics Research Scientist** advances the company’s robotics intelligence capabilities by inventing, validating, and transferring novel algorithms and learning-based methods into usable software components for real-world or simulated robots. The role blends deep research rigor (hypothesis-driven experimentation, publication-quality evaluation) with engineering pragmatism (reproducible code, measurable performance, integration-ready deliverables).

The **Senior Machine Learning Scientist** is a senior individual contributor responsible for designing, validating, and productionizing machine learning solutions that materially improve product capabilities and business outcomes. The role blends deep applied ML expertise with rigorous scientific method, strong software engineering habits, and pragmatic delivery in a modern software/IT operating environment.

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 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 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 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 **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 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 **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 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.