Sr. ML Ops Engineer

Corvus Robotics; United States; Hybrid; USD 100,000 - 130,000; Full time

Job summary

Corvus Robotics is hiring a Senior Software Engineer to build robust data infrastructure and internal tooling for our ML team. This role involves consolidating multiple data sources into a unified system and automating key processes to optimize efficiency.

The ideal candidate has 2-3 years of experience in production ML infrastructure and the ability to thrive in a dynamic startup environment. This position can be hybrid or remote, requiring occasional travel to Mountain View, CA.

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Qualifications

• 2-3 years of experience in shipping real production ML infrastructure for large datasets.
• Demonstrated ability to build distributed data pipelines.
• Experience in systems building from scratch or contributing significantly to small-team builds.

Responsibilities

• Build and maintain data pipeline infrastructure for ML.
• Develop tooling for dataset selection and curation.
• Automate model retuning loop for standard tasks.

Skills

Production ML infrastructure

Distributed data pipelines

Data flow understanding

Startup environment adaptability

Tools

Kubeflow

SLURM

Job description

About Corvus

Every physical good spends time in a warehouse, and every warehouse tracks their inventory. Today, nearly 100% of warehouses track their inventory manually using barcode scanners and climbing forklifts.

We’re Corvus Robotics. Our fully autonomous Corvus One™ drones use computer vision & robotics to automatically track inventory, improving worker safety and increasing labor efficiency. We believe that data-driven, safe inventory management will optimize the global physical economy and improve economic prosperity for humanity.

About the Role

With a growing fleet of autonomous drones and an expanding customer base, we’re now ready to multiply ML iteration speed and unblock more advanced ML product delivery.

We’re hiring a systems-oriented Senior Software Engineer to build the data infrastructure, training pipelines, and internal tooling that our ML team needs to move faster.

Specifically in this role you will:

• Build and maintain the data pipeline infrastructure that consolidates internal infra, labeling tools, S3, and other data sources into a unified, queryable system

• Build tooling for dataset selection and curation that can programmatically target specific data (by environment, object type, etc.)

• Own ML data infra from robot to training run, accessible to the ML team without backend engineering help

• Build model evaluation and regression testing infrastructure – real metrics, not vibes or "someone complained in prod"

• Automate the model retuning loop for standard tasks so ML engineers can be mostly hands‑off on routine updates

This is a hybrid or remote role with periodic trips to HQ in Mountain View, CA.

Must Haves

• 2‑3 years shipping real production ML infrastructure for big datasets, not just scripts

• Experience building distributed data pipelines that consolidate multiple sources

• Demonstrated understanding of data flow from raw collection, labeled training set, to trained models

• Experience building systems from scratch, or contributed heavily to a small‑team infra build where the playbook didn’t exist

• Ability to thrive in a startup environment with high ambiguity. You’ll figure out what to build

Nice to Haves

• Experience setting up annotation tooling and workflows

• Background in robotics autonomy and computer vision

Experience integrating with tools like Kubeflow, SLURM, or similar for scalable training workflows