Data Engineer

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SandboxAQ

Site WebLinkedInTous les Emplois

51 - 200 employés

Fondée en 2021

🤖 Intelligence artificielle

🔒 Cybersecurity

💊 Pharmaceutique

Artificial Intelligence • Cybersecurity • Pharmaceuticals

SandboxAQ est une entreprise développant des technologies d'IA quantitative et inspirées par la physique quantique pour résoudre des problèmes concrets dans les domaines de la découverte de médicaments, la science des matériaux, la cybersécurité et la navigation. Ils construisent de grands modèles quantitatifs (LMQ) fondés sur la physique et la chimie pour prédire les propriétés moléculaires, accélérer la conception thérapeutique et simuler des processus chimiques et catalytiques complexes, tout en appliquant leur expertise en IA et en cryptographie post-quantique pour améliorer la sécurité numérique et la navigation dans des environnements privés de GPS.

Description

• Data Pipeline Development & Maintenance: Work across a mixed-maturity pipeline environment • Data Modeling: Build and optimize data models that serve a diverse set of consumers. You'll make the data accessible and trustworthy, not just available • Simulation Data Integration: Work within the in-house simulation suite to add data-capturing capabilities and ensure simulation outputs feed cleanly into downstream pipelines alongside real-world field data • Data Quality & Observability: Instrument pipelines with quality checks, anomaly detection, and alerting so issues surface early • Cross-Functional Data Support: Translate ambiguous asks into well-defined requirements, repeatable datasets and lightweight Dashboards that the team can use independently going forward • Data Platform Infrastructure Contribution: Improve the features and reliability of our internal data platform over time • Documentation: Own the technical documentation for pipelines, data models, and schemas you touch. In a team this cross-functional, good documentation is a force multiplier

🎯 Exigences

• 3+ years of industry experience as a Data Engineer in a startup or fast-moving environment. • Strong proficiency in Python and SQL, with hands-on experience building production-grade data solutions. • Experience designing and maintaining data pipelines and data models/warehouses that process large, structured scientific or engineering datasets. • Hands-on experience building on AWS (e.g., S3, ECS, Lambda, IAM) combined with CI/CD and containerization (e.g., GitHub Actions or CircleCI, Docker) to automate, deploy, and maintain data and ML workloads in the cloud. • Practical MLOps experience: setting up and operating MLOps frameworks (e.g., MLFlow, DVC)

🏖️ Avantages

• Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions • Retirement savings with company matching • Paid parental leave • Inclusive family-building benefits • Flexible paid time off • Company-wide seasonal breaks • Support for flexible work arrangements that enable sustainable performance • Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs