Search Results for "SRE"

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 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 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 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 **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.