Job Description
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Company Overview
A leading construction technology company is advancing artificial intelligence and frontier machine learning capabilities through its AI-focused organization. The organization develops technology and data systems that support advanced machine learning research and applied AI products, with a focus on large-scale, multimodal, and spatial data.
Key Responsibilities
- Act as the technical lead for data engineering initiatives supporting frontier model research and applied ML systems.
- Design, build, and maintain scalable batch and streaming pipelines for multimodal data, including documents, images, and spatial metadata.
- Partner with ML researchers and system architects to translate experimental workflows into reliable and repeatable data systems.
- Lead the development of dataset curation, versioning, and lineage workflows that support rapid experimentation and reproducibility.
- Establish and maintain standards for data quality, validation, observability, and cost efficiency across AI data pipelines.
- Contribute to data architecture decisions spanning research environments and production systems.
- Identify gaps and inefficiencies in existing data workflows and conduct proofs of concept to evaluate potential improvements.
- Design data systems that allow datasets to move reliably from experimentation through production.
- Support the development of robust and observable data infrastructure for machine learning training, evaluation, and inference.
- Mentor engineers through code reviews, architecture discussions, design sessions, and hands-on collaboration.
- Provide technical guidance while remaining actively involved in implementation and engineering activities.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 8+ years of experience designing and operating complex data systems in production or research-adjacent environments.
- Strong proficiency in SQL and Python.
- Experience working with data-intensive and distributed systems.
- Proven experience building scalable data pipelines supporting machine learning training, evaluation, or inference workflows.
- Strong understanding of data modeling, dataset lifecycle management, and data quality best practices.
- Ability to operate effectively in ambiguous problem spaces and collaborate closely with researchers and architects.
- Demonstrated ability to provide technical leadership through direct engineering contribution, mentorship, and establishment of engineering standards.
- Strong communication skills with the ability to explain technical concepts and tradeoffs to both research and engineering audiences.
- Experience designing reliable, scalable, and production-ready data systems.
Preferred Qualifications
- Experience with large-scale dataset curation and annotation workflows.
- Familiarity with experiment tracking and reproducibility tooling.
- Experience with Databricks, Spark, lakehouse architectures, and cloud data warehouses.
- Experience with Kafka, Pub/Sub, or other event-driven data architectures.
- Familiarity with orchestration technologies such as Airflow or Dagster.
- Experience with data quality, data lineage, and observability tools.
- Experience with AWS or GCP cloud environments.
- Experience with containerized data workloads, CI/CD, and infrastructure-as-code.
- Experience optimizing data pipelines for GPU-backed machine learning training and large-scale inference workloads.
- Experience working with multimodal and spatial datasets.
- Familiarity with research-to-production machine learning workflows.
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