Senior Software Engineer, Data Platform (Remote-ok)

Andrenam is hiring the first dedicated engineer for its data platform. Today the platform is nascent — solid backend infrastructure exists, but nobody owns it, and the perception/ML team is being handed data that isn't in the shape they need. This is a founding-level, zero-to-one seat: you'll define how raw maritime signals become clean, consistent, labeled datasets for perception and foundation models.

You'll architect high-throughput pipelines that ingest real-time acoustic and telemetry data, then align, resample, calibrate, and restructure it — not necessarily in the moment (it can be matched up every 30 minutes, hour, or day) but into exactly what the ML team needs. Expect to build backfill/reprocessing frameworks, lineage and versioning for reproducibility, dataset discovery APIs, data-quality instrumentation, and — likely — a labeling tool from scratch. There's real backend infra to build on, but the shape of the platform is yours to define.

What You'll Own

• Post-processing pipelines that align, resample, and calibrate multi-sensor data
• Backfill/reprocessing frameworks for new filters, syncs, label corrections, and metadata enrichment across historical data
• Lineage and versioning to guarantee experiment reproducibility
• Dataset discovery + access APIs/SDKs (query by time, region, modality, labels, quality flags)
• Data-quality metrics, dashboards, and alerts; canary dataset builds Storage-layout optimization (columnar formats, compression, chunking, sharding, prefetching)
• Likely build a labeling tool and pre-labeling workflows for the perception team
• Ramp: month 2 — a first working pipeline in place; months 3–6 — iterating and honing it to exactly what the ML/perception team needs, plus the tooling around it

Requirements

• Strong data-pipeline architecture, high-throughput, with the ability to architect the system from a blank page
• Solid knowledge of at least one cloud provider, preferably AWS
• Comfortable deploying pipelines to the cloud
• Python + data tooling (PyArrow/Polars/Pandas, NumPy/SciPy), plus one of Go/Rust/TypeScript for services
• Bonus: full-stack/generalist range — able to build the tools the ML team needs end-to-end

Execution and Ownership

• Comes in and builds day one with minimal hand-holding
• Low ego; takes criticism without taking it personally
• High autonomy; startup-native

Background

• ~5–8 years; startup time counts double, 7+ if the experience is at slower-moving shops
• Architected data pipelines / greenfield data-platform work; dataset-as-a-product ownership is a strong signal
• Exposure to edge/sensor data (audio/sonar, video, telemetry), time sync, and geospatial context is a plus

Location and Visa

• Remote OK; LA strongly preferred, with occasional on-site visits to Torrance
• US citizenship required

Nice-to-Have

• Labeling workflows (interfaces, ontologies, consensus, QA) and label-store integrations
• Splitting/sampling strategy design (by time, platform, geography, class, SNR) to avoid leakage
• Orchestration (Airflow, Prefect) and metadata/lineage (MLflow, W&B)
• Dataset-as-a-product track record with strong lineage and documentation
• Athletic or competitive background (e.g., competitive chess) — reads as dynamic and startup-fit

Who Will Thrive Here

• The data engineer who wants to own an entire platform employee-early
• Architect-operators who can go from block diagram to shipped pipeline without hand-holding
• Zero-to-one builders who've stood up data platforms from scratch and like greenfield
• Low-ego, autonomous, comfortable in crunch — "get your stuff done"
• Excited by the mission and the messy, real sensor data of the ocean