Technical Staff Member – Data Intelligence

🕒 Maio 5

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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