Senior Data Infrastructure Engineer, Evolv Technology

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Software Engineering, or related field
• 2-3+ years of experience building production data pipelines and data platforms that support AI/ML models
• Strong proficiency in Python, C++ and distributed data processing frameworks
• Hands‑on experience with AWS services including S3, EC2, SageMaker, and Glue
• Experience designing data systems that support large‑scale ML training and experimentation
• Knowledge of data governance, access control, and lifecycle management
• Experience collaborating with ML, data science, operations, and cloud teams

Desirable

• Join Evolv as Senior Data Infrastructure Engineer in the Machine Learning & Sensors organization, responsible for building and operating the scalable, secure, and reliable data pipelines that power our AI/ML research and production systems
• In this role, you will own the end‑to‑end data lifecycle—from collection on thousands to millions of edge devices, through cloud ingestion and processing, into a centralized data factory enabling model training, evaluation, and continuous improvement
• Data is the backbone of our mission to deliver best‑in‑class AI‑based weapon detection systems
• You will ensure that data flows seamlessly across geographies, devices, and cloud systems while meeting strict requirements for quality, privacy, security, and scale
• This role is ideal for someone who thrives at the intersection of distributed systems, cloud pipelines, and ML‑driven data needs
• In the first 30 days:
• Develop a deep understanding of existing edge‑to‑cloud data pipelines and deployment environments
• Review current data ingestion flows, governance policies, and cloud infrastructure
• Assess pain points in data reliability, quality, and operational scalability
• Build relationships with AI/ML, data science, field operations, and cloud engineering teams
• Design and prototype data processing pipelines (both cloud and edge)
• Within the first three months:
• Design and implement improvements to core ingestion, validation, and processing pipelines
• Deploy scalable data pipeline with AWS‑based components (S3, EC2, Lambda, Glue, Step Functions, SageMaker integrations)
• Introduce automated validation workflows to detect corruption, missing metadata, or malformed data
• Design and implement automated model evaluation, model training and model improvement pipeline to speed up experiments
• Partner with field operations to improve data reliability, observability, and coverage across deployments
• By the end of the first year:
• Own the entire lifecycle of mission‑critical data pipelines supporting AI/ML research and production
• Architect next‑generation edge‑to‑cloud data systems that scale across millions of devices
• Define and enforce data governance frameworks including retention, access control, privacy, and lineage
• Enable ML teams to rapidly experiment through high‑quality, discoverable, versioned datasets
• End‑to‑End Data Pipeline Ownership:
• Design, build, and maintain both research and production data pipelines spanning edge devices, cloud services, and centralized data platforms
• Own the full data lifecycle: collection, ingestion, processing, obfuscation, versioning, access, retention, and retirement
• Edge‑to‑Cloud Data Flow:
• Develop resilient ingestion pipelines capable of handling variable connectivity and device heterogeneity
• Support secure data transfer from the field to cloud storage systems
• Collaborate with field ops to enhance data coverage, observability, and operational robustness
• Data Quality, Governance & Compliance:
• Implement privacy‑preserving transformations and obfuscation pipelines
• Build automated cleaning/validation steps to remove duplicates, detect corruption, and validate metadata
• Establish data lineage, retention policies, and access controls ensuring compliance and traceability
• Data Services for AI/ML:
• Provide scalable data services for model training, evaluation, and research experimentation
• Support continuous data refresh and retraining workflows
• Integrate with data labeling services and annotation workflows
• Enable efficient access patterns for large‑scale ML workloads
• AWS‑Based Cloud Infrastructure:
• Build and optimize pipelines using AWS services (S3, EC2, SageMaker, Lambda, Glue, Step Functions)
• Design for cost‑efficiency, performance, and reliability at scale
• Collaboration & Feedback Loops:
• Partner with AI/ML engineers, scientists, and data scientists to understand data requirements
• Translate feedback into automated improvements in data collection, labeling, and consumption
• Support cross‑functional teams in exploratory analysis and debugging data issues
• Scaling the Data Factory:
• Design and manage data schema, data versioning and data factory updates
• Architect systems that scale globally across millions of devices
• Ensure the data platform remains flexible for research and reliable for production operations
• Example Problems You Will Own
• Design a resilient global ingestion pipeline aggregating sensor data from millions of devices
• Build ML‑ready data services enabling easy discovery, versioning, and consumption of datasets
• Implement automated validation and cleaning workflows that dramatically reduce bad data
• Define and enforce lifecycle and governance policies across research and production datasets
• You will join our R&D organization, reporting directly to VP of ML and sensors
• In this role, you will interface with cross-disciplinary teams of highly skilled and autonomous engineers with expertise in Electromagnetics, Computer Vision, and AI
• Our R&D organization includes more than 100 dedicated developers, engineers, scientists, managers and directors, each bringing deep technical knowledge and a strong culture of collaboration and support
• The team culture is one based on building trust, collaboration, on-going development through kindness, authenticity, courage, drive, and fun!
• This role is based at our headquarters in Waltham, Massachusetts. Due to the nature of our software-enabled hardware products, this position requires a minimum of 60% or 3 days per week on-site work