About The Position

SandboxAQ's AQNav (Quantum Navigation) team builds global, GPS-independent, navigation solutions for military and commercial aircraft, and autonomous submersible platforms. This role is an opportunity to work on cutting-edge technology, building infrastructure that empowers the team with necessary data, pipelines for faster progress, and processes to accelerate and validate models. The tools and pipelines built will directly impact the team's success. The ideal candidate thrives in a multi-hat role, collaborating with a diverse group of physicists, ML researchers, hardware engineers, software engineers, data engineers, field ops personnel, navigation engineers, customer-facing leaders, and managers.

Requirements

  • 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).

Nice To Haves

  • A Master’s or Ph.D. in a specialized technical field like computer science, data science, mathematics, etc.
  • Experience working with sensor data (100-1KHz range).
  • Ability to build interactive dashboards in Hex or similar.
  • Experience working with standard ML libraries like PyTorch, scikit-learn and basic supervised/ unsupervised learning techniques.

Responsibilities

  • 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, ensuring data is accessible and trustworthy.
  • Simulation Data Integration: Enhance the in-house simulation suite with data-capturing capabilities and ensure simulation outputs integrate cleanly into downstream pipelines alongside real-world field data.
  • Data Quality & Observability: Implement quality checks, anomaly detection, and alerting within pipelines to ensure early issue surfacing.
  • Cross-Functional Data Support: Translate ambiguous requests into well-defined requirements, repeatable datasets, and lightweight dashboards for independent team use.
  • Data Platform Infrastructure Contribution: Enhance the features and reliability of the internal data platform.
  • Documentation: Own the technical documentation for pipelines, data models, and schemas.

Benefits

  • Competitive base salary, performance-based incentives or bonuses (where applicable), and equity participation.
  • 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.
  • Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs.
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