Data and ML Infrastructure Engineer

This is a fully remote position, open to applicants in Rhode Island.

📋 Description

• Develop and sustain infrastructure for video, imagery, telemetry, sensor data, autonomy logs, mission data, and field-test data.

• Oversee data ingestion, storage, indexing, metadata, access patterns, and lifecycle management within HavocAI’s data lake.

• Create scalable pipelines that convert raw operational data into curated datasets suitable for ML training, evaluation, debugging, and analysis.

• Design tools for searching, filtering, tagging, and retrieving data across various platforms, missions, operating conditions, and events.

• Engineer infrastructure that can efficiently and reliably manage large volumes of multimodal operational data.

• Develop workflows to select, clean, label, validate, and version datasets.

• Collaborate with Autonomy, Perception, Software, and Field Operations teams to pinpoint high-value data for model development and system evaluation.

• Assist in annotation and labeling workflows for video, imagery, tracks, telemetry, and other ML inputs.

• Create reproducible dataset-generation workflows for training, validation, regression testing, and benchmarking.

• Integrate datasets and data infrastructure with model training, experiment tracking, evaluation, and deployment workflows.

• Facilitate the construction of multimodal datasets, ensuring synchronization and alignment across sensors and data streams.

• Develop automated checks for missing streams, corrupted files, synchronization issues, metadata gaps, labeling errors, and pipeline failures.

• Set standards for dataset quality, lineage, versioning, and reproducibility.

• Establish monitoring and observability around critical data pipelines and infrastructure.

• Diagnose complex data and infrastructure issues and drive them to resolution.

• Utilize field data, logs, and test results to assist engineering teams in understanding system performance and identifying areas for improvement.

• Create self-service tools that enhance the discoverability, accessibility, analysis, and usability of operational data for engineers.

• Work closely with Autonomy, Perception, Software, Simulation, Field Operations, and Program teams.

• Translate engineering and ML requirements into scalable data capabilities.

• Enhance workflows for replaying, visualizing, analyzing, and comparing operational data.

• Maintain comprehensive documentation, data standards, and best practices for internal data usage, governance, and security.

• Within the first year, establish reliable field-data pipelines, enhance data discoverability, create reproducible dataset workflows, improve quality and observability, and facilitate faster model improvement and deployment.

⛳️ Requirements

• Bachelor's degree in Computer Science, Data Science, Machine Learning, Electrical Engineering, Computer Engineering, Robotics, Applied Mathematics, or a related technical discipline.

• 3+ years of experience in data engineering, ML infrastructure, data platforms, backend systems, MLOps, or similar engineering roles.

• Proven experience in designing and managing production data pipelines for large-scale structured, semi-structured, or unstructured datasets.

• Familiarity with video, imagery, time-series telemetry, sensor data, logs, or other high-volume operational data.

• Proficient programming skills in Python and SQL.

• Experience with cloud storage, object stores, data lakes, databases, distributed processing, or modern data platforms.

• Understanding of dataset versioning, metadata management, data lineage, access controls, and reproducible data workflows.

• Strong software engineering principles, including testing, reliability, maintainability, and observability.

• Excellent debugging skills and comfort with complex data pipelines and production infrastructure.

• Capability to work independently and take ownership in a fast-paced engineering environment.

• U.S. citizenship and the ability to obtain and maintain a U.S. Government security clearance.

• Preferred: experience with ML infrastructure, MLOps, training pipelines, experiment tracking, model evaluation, or model registries.

• Preferred: familiarity with S3-compatible storage, PostgreSQL, Spark, Ray, Airflow, Dagster, Kubernetes, Docker, or Kafka.

• Preferred: experience with data catalogs, dataset versioning platforms, feature stores, or labeling tools.

• Preferred: experience in building search, replay, visualization, or analysis tools for video, telemetry, logs, or sensor data.

• Preferred: experience supporting annotation workflows for computer vision, perception, tracking, or autonomy.

• Preferred: knowledge of sensor synchronization, timestamp alignment, calibration metadata, log replay, or multimodal dataset construction.

• Preferred: experience with security, access controls, auditability, and data-handling requirements in government or defense contexts.

• Preferred: experience supporting defense, robotics, autonomy, aerospace, or dual-use technology initiatives.

• Preferred: active or prior security clearance.

🏝️ Benefits

• 100% Employer-paid Health, Dental, and Vision Insurance for you and your families.

• Life Insurance (Employer Paid).

• Opportunity to participate in the company's 401k program (Matching).

• Unlimited PTO policy with a mandated 2-week minimum.

• Equity Package.

• Work / Home Office Stipend.

• Global Entry.

• 16 Week Paid Parental Leave.

• Monthly Health and Wellness Stipend.

• Bonus.