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# Senior Software Engineer, Data Platform

- **Company:** NxT Level
- **Location:** Torrance, CA, United States
- **Experience:** Expert
- **Salary:** $200,000.0 - $240,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Data Analysis, Big Data, Data Discovery, Data Infrastructure, Information Leak Prevention, Shard (Database Architecture), Hardware Interface Design, Python (Programming Language), Metadata, NumPy, SciPy, TypeScript, Management of Software Versions, Workflow Management Systems, Pandas, Data Management, Machine Learning Operations, Api Design, Data Pipelines
- **Published:** September 3, 2026
- **Apply:** https://www.wayup.com/i-j-Senior-Software-Engineer-Data-Platform-NxT-Level-931436439602876/

## About the Role

+ Strong experience building data platforms, ML infrastructure, or large-scale data processing systems
 + Experience owning dataset quality, reproducibility, lineage, and evaluation fidelity
 + Strong Python experience and comfort with modern data tooling
 + Experience with tools such as PyArrow, Polars, Pandas, NumPy, SciPy, or similar libraries
 + Experience working with audio, vision, telemetry, sensor, or time-series data
 + Strong understanding of data pipelines, dataset versioning, metadata, and quality monitoring
 + Experience building APIs, SDKs, or access layers for data discovery and consumption
 + Ability to design systems that support both real-time and historical reprocessing workflows
 + Comfort working closely with ML, perception, hardware, and product teams
 + Strong ownership, technical judgment, and ability to operate in a fast-moving startup environment
Bonus Experience

 + Experience owning "dataset-as-a-product" systems used across multiple model families
 + Experience building curated corpora with strong lineage, documentation, and reproducibility
 + Experience designing dataset splitting or sampling strategies by time, platform, geography, class, signal quality, or similar dimensions
 + Experience preventing data leakage and improving model generalization through thoughtful dataset design
 + Hands-on experience with labeling workflows, ontologies, consensus systems, QA, or label-store integrations
 + Experience with Go, Rust, or TypeScript for services
 + Experience with orchestration tools such as Airflow or Prefect
 + Experience with metadata and lineage tools such as MLflow or Weights & Biases
 + Exposure to edge or sensor data, including audio, sonar, video, telemetry, time synchronization, or geospatial context
 + Experience in defense, maritime, autonomy, robotics, aerospace, or national security technology

## Description

Our client is hiring a Senior Software Engineer, Data Platform to help build the real-time ML data platform behind autonomous and semi-autonomous marine systems.
This is a high-impact role for an engineer who is passionate about autonomy, maritime sensing, defense technology, and large-scale data infrastructure. You'll help define how raw maritime signals become clean, consistent, labeled datasets that power perception models, foundation models, evaluation systems, and AI-driven analytics.
The core challenge is building a platform that can ingest, process, store, reprocess, and analyze data from thousands of marine systems across a wide range of sensors, hardware interfaces, and data sources - all with a high bar for security, reliability, reproducibility, and performance.
What You'll Do

 + Build and operate a real-time ML data platform for autonomous and semi-autonomous marine systems
 + Define how raw maritime signals become clean, consistent, labeled datasets for perception and foundation models
 + Build post-processing pipelines that align, resample, and calibrate multi-sensor data
 + Create robust backfill and reprocessing frameworks for historical data
 + Apply new filters, synchronizations, label corrections, and metadata enrichments across large datasets
 + Establish lineage, versioning, and reproducibility standards for ML experiments
 + Build dataset discovery and access APIs/SDKs for downstream users
 + Enable teams to query datasets by time, region, modality, labels, and quality flags
 + Instrument data quality metrics across completeness, corruption, drift, label density, class balance, and regional coverage
 + Build dashboards, alerts, and canary dataset builds to improve data reliability
 + Optimize storage layouts for fast reads, parallelization, compression, chunking, sharding, locality, and prefetching
 + Partner closely with ML, perception, hardware, and product teams to map data contracts to model needs


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