Job description

Who we are Neros is a defense technology company rebuilding America’s drone industrial base. We design and manufacture high-performance unmanned systems that are tested in combat, iterated at startup speed, and built at massive scale. Our team culture is fast, hands-on, and obsessed with closing the gap between design and deployment. As drones transform the character of warfare, Neros is delivering the systems the West needs to compete on the modern battlefield and deter the adversaries of democracy. We’re hiring engineers, operators, and builders who want to move fast, take on extreme ownership, and get capability into the hands of warfighters in months, not years. What you will be doing The Data Platform Engineer owns the autonomy data pipeline end to end: recovering flight data from vehicles in the field, landing it in the cloud, and making it searchable and replayable at scale. This is a greenfield role. You will stand up the storage, catalog, and query infrastructure that every autonomy test, simulation run, and machine learning dataset at Neros depends on, and you will own its architecture, operating cost, and compliance posture. Responsibilities Build the path that brings flight data back from the field, including triggered capture on the vehicle, prioritized upload so the highest-value flights return first, and resumable transfer with integrity verification. Turn raw logs into a usable corpus: decode, time-align multi-sensor and video streams, validate, and quarantine malformed data before it reaches downstream users. Design the catalog and tag model that index the corpus, and stand up the cloud storage and database that hold it Build the query layer so an engineer can retrieve every flight matching a condition, for example loss of target lock at terminal stage under high glare within the last 90 days, and get playable video back in seconds. Serve logs to the evaluation harness with stable ordering, exact time alignment, and reproducible results across runs, so a regression job can run over thousands of flights at once. Build versioned, immutable datasets from catalog queries, with lineage recorded so any model training set can be rebuilt exactly months later. You should have the following 5+ years building production data or backend infrastructure, including at least one system you owned end to end from initial design through ongoing operation Direct experience with large-scale log or sensor data: multi-terabyte and growing, with video and multiple synchronized sensor streams (rosbag, MCAP, HDF5, Parquet, or equivalent formats), rather than row-oriented business data Designed and owned a data schema, index, or catalog that other engineers queried daily, and lived with the consequences of that design, including at least one migration Strong Python, plus SQL and working ownership of a relational database (PostgreSQL or equivalent) used in production Practical experience with cloud object storage and compute (Azure, AWS, or GCP) and the ability to provision and operate it independently, without a dedicated platform or DevOps team Experience with distributed or parallel batch processing and job orchestration (Spark, Ray, Dask, Airflow, Dagster, or equivalent) across large volumes of recorded dat Working knowledge of time synchronization and alignment across sensor streams, and of deterministic, reproducible processing of recorded data A track record of building internal tooling that other engineers adopted, including at least one case where you changed the design based on how it was actually being used Nice to have Log or data infrastructure built for robotics, autonomous vehicles, aerospace, or defense programs, where replay determinism, time alignment, and multi-sensor logs are native problems rather than new ones Experience handling video at scale, including transcoding, frame-accurate seeking, and streaming playback of recorded footage to engineering users Direct Azure experience, infrastructure defined as code (Terraform or Bicep), and prior responsibility for a cloud budget where storage tiering and egress costs mattered Prior work in export-controlled, GovCloud, or IL4/IL5 environments, or familiarity with CMMC and ITAR data handling obligations Experience connecting a data pipeline to a labeling vendor or internal labeling tooling, or to model training, experiment tracking, and model registry workflows US Salary Range $163,500 – $228,500 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are considered part of Neros' total compensation package. We’re an equal opportunity employer. We welcome all applicants without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Requirements

Must-haves

Build the path that brings flight data back from the field, including triggered capture on the vehicle, prioritized upload so the highest-value flights return first, and resumable transfer with integrity verification.
Turn raw logs into a usable corpus: decode, time-align multi-sensor and video streams, validate, and quarantine malformed data before it reaches downstream users.
Design the catalog and tag model that index the corpus, and stand up the cloud storage and database that hold it
Build the query layer so an engineer can retrieve every flight matching a condition, for example loss of target lock at terminal stage under high glare within the last 90 days, and get playable video back in seconds.
Serve logs to the evaluation harness with stable ordering, exact time alignment, and reproducible results across runs, so a regression job can run over thousands of flights at once.
Build versioned, immutable datasets from catalog queries, with lineage recorded so any model training set can be rebuilt exactly months later.
You should have the following
5+ years building production data or backend infrastructure, including at least one system you owned end to end from initial design through ongoing operation
Designed and owned a data schema, index, or catalog that other engineers queried daily, and lived with the consequences of that design, including at least one migration
Strong Python, plus SQL and working ownership of a relational database (PostgreSQL or equivalent) used in production
Practical experience with cloud object storage and compute (Azure, AWS, or GCP) and the ability to provision and operate it independently, without a dedicated platform or DevOps team
Experience with distributed or parallel batch processing and job orchestration (Spark, Ray, Dask, Airflow, Dagster, or equivalent) across large volumes of recorded dat
Working knowledge of time synchronization and alignment across sensor streams, and of deterministic, reproducible processing of recorded data
A track record of building internal tooling that other engineers adopted, including at least one case where you changed the design based on how it was actually being used
We’re an equal opportunity employer. We welcome all applicants without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Nice-to-haves

Log or data infrastructure built for robotics, autonomous vehicles, aerospace, or defense programs, where replay determinism, time alignment, and multi-sensor logs are native problems rather than new ones
Experience handling video at scale, including transcoding, frame-accurate seeking, and streaming playback of recorded footage to engineering users
Direct Azure experience, infrastructure defined as code (Terraform or Bicep), and prior responsibility for a cloud budget where storage tiering and egress costs mattered
Prior work in export-controlled, GovCloud, or IL4/IL5 environments, or familiarity with CMMC and ITAR data handling obligations
Experience connecting a data pipeline to a labeling vendor or internal labeling tooling, or to model training, experiment tracking, and model registry workflows

Application prep

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Keywords to mirror

machine learningpythonsqlazureawsgcp

Platform: greenhouse
Effort: quick

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