Data Infrastructure Engineer

Pivotrobotics

San Francisco, CA

On-site

Who this role is best for

Aimed at mid-level data engineers who design and implement data systems for robotics and industrial IoT, with a focus on AWS and ML infrastructure. The role also values candidates who can manage edge-to-cloud pipelines and are open to on-site work at customer locations.

Best fit for

Mid-level data engineers with experience in AWS and ML infrastructure for robotics or industrial IoT
— “Build and maintain data infrastructure on AWS”
Individuals comfortable working across the data stack from analysis to system design
— “Ability to work across the stack, from analyzing data to designing the systems around it”

Things to consider

Travel to customer sites is expected, which may require flexibility with location and schedule
— “Willingness to travel to customer sites”
The role requires a balance between data modeling and ML infrastructure implementation
— “Build ML infrastructure: training pipelines, dataset versioning, and model deployment”

How to stand out

Highlight experience with edge computing and artifact stores in your resume and interview responses
— “Build the artifact store for scans, meshes, model checkpoints, and calibration files”
Emphasize your ability to design systems that account for distributed tradeoffs
— “Strong schema design and data modeling, including distributed systems tradeoffs”
Showcase projects where you implemented end-to-end data pipelines from edge to cloud
— “Build edge-to-cloud pipelines between robotic cells and our infrastructure”

Pace · Fast PacedCollaboration · MediumAutonomy · MediumDecision Impact · TeamLevel · Mid

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

owns data infrastructure on AWS
builds and maintains data infrastructure
designs and owns schemas and data models

Typical background

2+ years building data infrastructure on AWS

Skills & requirements

Required

AWSSchema DesignData ModelingDistributed SystemsML InfrastructureData Infrastructure

Preferred

RoboticsIndustrial IotManufacturing

Stack & domain

AWSSchema DesignData ModelingDistributed SystemsML InfrastructureData AnalysisSystem DesignRoboticsAutonomyIndustrial Iot

About the role

Original posting from Pivotrobotics via Ashby

Responsibilities
Design and own the schemas and data models behind our production systems
Build and maintain data infrastructure on AWS
Build the artifact store for scans, meshes, model checkpoints, and calibration files
Build edge-to-cloud pipelines between robotic cells and our infrastructure
Build ML infrastructure: training pipelines, dataset versioning, and model deployment
Requirements
2+ years building data infrastructure on AWS (application-side, not platform operations)
Strong schema design and data modeling, including distributed systems tradeoffs
ML infrastructure experience
Ability to work across the stack, from analyzing data to designing the systems around it
Willingness to travel to customer sites
Preferred
Data infrastructure in a robotics, autonomy, or industrial IoT setting
Edge computing experience
Interest in manufacturing

Source: Pivotrobotics careers (Ashby)