H

Hyper

Venture capital empowering startups with resources

Intelligent Systems Platform Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeRichmond, VA, USA$130k - $150k /yrIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science, Electrical Engineering, Systems Engineering, or a related field is required; a master's degree is preferred.
  • At least 5 years of experience designing and operating industrial Internet of Things platforms, data pipelines, or operational technology/information technology data infrastructure.
  • Strong experience with data modeling, schema governance, and high-throughput data pipeline design.
  • Experience designing and implementing polyglot data architectures across multiple data types.
  • Hands-on experience building end-to-end data pipelines for machine learning and generative artificial intelligence workflows.
  • Familiarity with industrial communication protocols such as Modbus, BACnet, Simple Network Management Protocol, Open Platform Communications Unified Architecture, and Message Queuing Telemetry Transport.
  • Experience working with cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform for data and machine learning workloads.
  • Experience collaborating with data scientists and supporting production machine learning model deployment.
  • A strong understanding of data security, governance, and observability practices.
  • Applicants must be authorized to work in the United States without current or future sponsorship.

Responsibilities

  • Design, build, and operate the AI-native Industrial Internet of Things platform end-to-end.
  • Architect and manage a polyglot data platform using appropriate storage technologies for time-series, relational, unstructured, and vector data.
  • Build and maintain industrial data pipelines that integrate Internet of Things telemetry, engineering data, field service records, and original equipment manufacturer documentation.
  • Develop scalable data pipelines supporting traditional machine learning and generative artificial intelligence workflows.
  • Define and enforce data quality, governance, observability, and security standards across the platform.
  • Design and maintain industrial data models and asset frameworks supporting monitoring, benchmarking, and trend analysis.
  • Partner with data scientists to enable predictive maintenance, anomaly detection, and fault classification models in production.
  • Collaborate with analytics product managers to deliver data products such as condition-based maintenance, fleet optimization, and energy efficiency insights.
  • Translate customer and operational requirements into platform capabilities and data products.
  • Capture and structure engineering and field service knowledge into standardized platform inputs.
  • Evaluate and select platform components, including Internet of Things gateways, edge devices, cloud services, and data infrastructure tooling.
  • Manage vendor and technology partner relationships to ensure alignment with the platform roadmap and delivery expectations.
  • Embed security best practices aligned with International Electrotechnical Commission 62443 and operational technology/information technology convergence standards into platform design and operation.
  • Maintain platform documentation, including architecture diagrams, data lineage, and onboarding materials.
  • Partner with Sales and Product teams to align platform capabilities with go-to-market strategy and customer outcomes.

Desired Qualifications

  • Experience with predictive maintenance, condition monitoring, or industrial analytics systems.
  • Familiarity with supervisory control and data acquisition, data center infrastructure management, building management systems, or computerized maintenance management systems platforms.
  • Experience applying generative artificial intelligence and retrieval-augmented generation techniques to technical or industrial datasets.
  • Experience with containerized environments and continuous integration/continuous delivery pipelines for data platforms.
  • Familiarity with data lakehouse or federated data architectures.
  • Experience with data observability and monitoring tools.
  • Knowledge of International Electrotechnical Commission 62443 or similar industrial cybersecurity frameworks.
  • Relevant certifications such as AWS Machine Learning Specialty, Azure IoT Developer, or similar.

About the Company

Hyper

Venture capital empowering startups with resources

Hyper acts as a venture-focused partner for modern startups, offering more than money. It provides access to a community, distribution channels, mentorship, talent recruitment, and fundraising support to help startups grow. How it works: Hyper connects startups with a network of mentors and peers, helps them spread their product through distribution partnerships, supports hands-on mentorship programs, assists in finding top talent, and guides fundraising efforts. Difference from competitors: Unlike funds that only provide capital, Hyper offers ongoing hands-on support and a strong network across community, distribution, and talent, giving startups practical help to scale. Goal: To help startups succeed and grow by supplying the resources and network they need beyond cash.

Company size
11-50
Company stage
Not listed
Total funding
$79.6M
Headquarters
San Francisco, CA
Founded
2020

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