Staff Data Engineer

About the role

LVT is redefining how businesses operate in the physical world, moving beyond traditional security solutions to deliver AI-driven, actionable intelligence that makes sites smarter, safer, and more secure. Since pioneering our first mobile, solar-powered units, our commitment to scrappy, hands-on innovation has made us an established leader and one of the fastest-growing companies in intelligent site technology. We are building the next generation of solutions—from our physical units in the field to a powerful Agentic AI platform—that allows our customers to gain unprecedented visibility and control over safety, compliance, and operations.

Responsibilities

• Data Flywheel Ownership: Own the end-to-end loop that converts raw edge telemetry and video into labeled training data, frozen evaluation sets and feeds model outputs back into the next round.
• Layered Dataset Pipelines: Build and own the pipelines that register raw source data, standardize it into a single well-defined schema, and join and aggregate it into curated datasets so every team trains, validates, and benchmarks from one consistent store through one reader, rather than copying and reformatting data per use case.
• Labels & Annotation Data Lifecycle: Own how labels and semantic annotations are appended to datasets without rewriting source data, then versioned, quality-checked, and served, partnering with annotation and data-operations teams on label production and verification while you own the dataset, storage, and serving side.
• Evaluation & Benchmark Sets: Own the frozen, versioned validation and benchmark datasets that make model comparisons valid over time stable enough that an accuracy delta reflects the model, not a shifting dataset including the review and scrubbing discipline required before any set is shared externally.
• Dataset Versioning: Own schema and content versioning so producers can evolve datasets without breaking consumers opt-in versions, append-without-rewrite for new fields, and the reader/writer indirection that lets data migrate underneath clients on a controlled rollout instead of forced lockstep migrations.
• Framework Integration & Self-Serve Access: Own the read/write libraries and integrations researchers depend on PyTorch/Lightning dataloaders, a simple record-level CRUDL API, and Spark/analytics access and self-service so AI teams stay focused on model development.
• Governance Enforced: Make governance machine-enforced in the flywheel rather than documented after the fact classification of clips, frames, labels, and embeddings; scrubbing and anonymization in load jobs; and lineage and provenance for every dataset version, annotation campaign, and training input.
• Technical Mentorship: Set the data-engineering standards for the flywheel schema conventions, dataset contracts, quality gates and mentor IC work toward them, growing the function as the team forms.

Qualifications

• Data Engineering Depth: 8+ years building and operating large-scale data pipelines and data-lake or lakehouse systems in production ingestion, ETL/ELT, partitioning and storage-format decisions, and the reader/writer libraries consumers rely on.
• ML Data Specialty: Has built data pipelines for model training and evaluation, labeled data, and evaluation/benchmark sets with a working understanding of how data quality and versioning move model results.
• Lakehouse Architecture: Strong experience with medallion-style layered data architectures and modern table/lake formats (e.g. Iceberg, Delta, Parquet, or comparable), including schema evolution and dataset versioning.
• Multimodal Data at Scale: Experience with large multimodal data video, image, sensor/telemetry and the storage and access patterns that make it queryable at scale (denesting, repartitioning, binary-inline vs. reference storage).
• Framework Integration: Hands-on with the data side of ML frameworks PyTorch/Lightning dataloaders and Spark and strong Python knowledge.
• Governance & Provenance: Practical experience enforcing data governance in pipelines classification, access control, lineage and provenance, retention, particularly for privacy sensitive data.
• Technical Leadership: A track record of setting data-engineering direction and leveling up engineers (technical leadership; formal management not required).
• Education: Bachelor's or Master's in Computer Science, Engineering, or a related field, or equivalent practical experience.

Benefits

• Comprehensive health, dental and vision coverage
• Retailment benefits (401k match up to 4%)
• Flexible PTO