Founding Machine Learning / Data Engineer

About the roleRunning CI on real hardware produces a kind of data most ML teams never get to touch: power traces, sensor streams, build artifacts, console logs, test results, all tied to specific physical machines doing specific physical work. There's serious signal in there — predicting flaky boards, classifying failure modes, scoring job risk, and eventually closed-loop optimization of how the fleet itself is used. We're hiring a founding ML / data engineer to build the pipeline that turns that data into models, and the models into product features. You'll own this end-to-end — not "hand off a notebook and hope." Data ingestion, labeling, training infrastructure, evaluation, deployment, monitoring. You'll set the technical direction for ML at Primitive and shape what good looks like — from the first models in production to ML as a core part of the product. This is the first dedicated ML hire. It's a build-from-zero role on top of a rich, real-world dataset. What you'll doDesign and build the data platform end-to-end: extend instrumentation where signals are missing today, then ingest from Postgres, SeaweedFS / S3, and streaming telemetry into a clean, versioned analytical layer Build the labeling workflow that lets us (and eventually customers) label hardware events without it becoming a permanent side project Design and operate a reproducible training stack on AWS — distributed where it needs to be, with experiment tracking, dataset versioning, and a real eval harness Ship inference for product features: low-latency serving where it matters, batch scoring where it fits Operate models in production: drift monitoring, regression gates, the dashboards that tell us when a model is silently rotting Partner with the full-stack and hardware teams to integrate predictions cleanly into the product surface Set the bar for ML rigor at Primitive: eval-first development, reproducibility, honest reporting of model quality Mentor new engineers on data and ML patterns as the team grows; raise the bar on data contracts, eval design, and reviewability About you5+ years in ML / data engineering, with at least one production ML system you took from raw data to served predictions Strong Python; comfortable with modern ML tooling (PyTorch, Hugging Face, Ray, or equivalents — we're not religious) Real opinions about data versioning, feature stores, and experiment tracking — you've used DVC, LakeFS, MLflow, or Weights & Biases and know what each is good and bad at Production data pipeline experience with a real data warehouse — schema design, contracts, ownership AWS chops: S3, EKS-hosted training (or SageMaker), IAM that doesn't terrify the security team Built or operated a human-in-the-loop labeling workflow Comfortable setting architectural direction for ML/data at a small company, balancing vision with pragmatism BonusTime-series, sensor, or signal-processing ML — we have a lot of it LLM fine-tuning, retrieval, or agent eval experience (there's product surface here too) Background in hardware, EE, or anything physical — helps a lot when the data is from real machines ClickHouse, dbt, Airflow / Dagster / Prefect at production scale Contributed to open-source ML or data tooling StackPython, PyTorch, AWS (S3, EKS, possibly SageMaker), PostgreSQL, SeaweedFS, Grafana / Mimir. Pipeline orchestration is open (Airflow / Dagster / Prefect). How we workOffices inNew York, NYandSan Francisco, CA— flexible in-office attendance, no fixed days per week We like working together in person: regular team meetups across both offices Small team, high ownership — most engineers ship to production in their first week Light on-call for serving infrastructure once models are in production BenefitsHealth, dental, and vision for you and your dependents Substantial equity, with early exercise and an extended post-termination exercise window Unlimited paid vacation, plus local holidays Equinox membership CompensationBase salary:$180,000 – $250,000 , adjusted based on location, level, and experience. Total compensation includes substantial equity and the benefits above. A note on applyingIf you don't tick every box on the list above, apply anyway. The bullets describe the engineer we'd be thrilled to hire; what we actually need is someone who can do the work and learn the rest. We especially encourage applications from people who don't see themselves represented in tech today.#J-18808-Ljbffr