Technical Staff Member – Data Intelligence

🕒 Maio 5

Candidatar-se

Encontrar Vagas Remotas Similares

Receber Emails de Vagas Remotas

📊 Verifique sua pontuação de currículo para esta vaga

Melhore suas chances de conseguir uma entrevista verificando sua pontuação de currículo antes de se candidatar.

Enviar Currículo

Reka AI

SiteLinkedInTodas as Vagas

1 - 10 funcionários

Fundada em 2022

🤖 Inteligência Artificial

🏢 Corporativo

☁️ SaaS

💰 $58.000.000 Series A em 2023-06

Artificial Intelligence • Enterprise • SaaS

Reka AI é uma empresa especializada no desenvolvimento de modelos de IA multimodal de ponta. Esses modelos são projetados para capacitar agentes de IA com a capacidade de ver, ouvir e falar, facilitando uma gama de aplicações que vão desde texto e código até imagens, vídeos e dados de áudio. Os modelos proprietários da Reka, incluindo Reka Core, Flash, Edge e Spark, são criados do zero com arquiteturas inovadoras que permitem a implantação em dispositivos, no local ou na nuvem. Conhecida por avançar no campo da pesquisa em IA, a Reka AI colabora com parceiros tecnológicos globais para expandir o acesso às suas avançadas capacidades de IA multimodal.

Descrição

• Work with model researchers to define what “good data” means for our models, including quality metrics, validation checks, and acceptance thresholds • Explore open source datasets and create internal ones most suitable to build fundamental World Models • Build algorithms for automated data quality assessment, data domain mixtures, and domain adaptation from synthetic to real data. • Track datasets, metadata, provenance, and versions so experiments are reproducible and it’s clear what data went into which training and evaluation runs • Own CI/CD and development tooling for the data stack (GitHub, Python, PyTorch), and automate repetitive workflows to reduce friction • Track and optimize throughput, storage, and compute utilization across pipelines and related assets

🎯 Requisitos

• Strong ML and deep learning fundamentals with experience building and operating large-scale data and/or compute systems • Comfortable moving between research questions and production engineering: you can dig into data, run analyses, and also ship reliable systems • Demonstrated research experience with data compositions, quality, and dataset releases • Ability to design and execute experiments with convincing unbiased outcomes • Practical experience with distributed processing and orchestration (Spark, Ray, Airflow, or equivalents) • Solid Python skills, and familiarity with the tooling around modern model training workflows (datasets, checkpoints, experiment tracking) • Strong instincts around data quality: how to measure it, how to monitor it, and how to prevent regressions as things scale • Able to work in a fast-moving environment, prioritize what matters, and communicate clearly with both researchers and engineers • Bonus: experience with large video datasets, dataset curation for training, or building internal tooling for evaluation/analysis in ML environments

🏖️ Benefícios

• Flexible work arrangements