Founding Machine Learning / Data Engineer

Primitive Instruments Corporation; New York, Northern (NY, KY); Hybrid; USD 180,000 - 250,000; Full time

Benefits offered by this job

Health insurance

Equity (substantial)

Unlimited vacation

Equinox membership

Job summary

Primitive Instruments Corporation is hiring its founding ML / data engineer to design and own the data platform end-to-end. You will ingest data from Postgres, SeaweedFS/S3, and telemetry, build a labeling workflow, and run reproducible training stacks on AWS with experiment tracking and versioned datasets.

You will ship low-latency inference, monitor drift, and collaborate with hardware and full-stack teams to embed predictions into the product surface.

Qualifications

• 5+ years in ML / data engineering with a production ML system
• Proficient Python and modern ML tooling (PyTorch, Hugging Face, Ray)
• Experience with data versioning, feature stores, and experiment tracking

Responsibilities

• Design and build end-to-end data platform and labeling workflow
• Operate a reproducible training stack on AWS with experiment tracking
• Ship low-latency inference and batch scoring for product features
• Monitor models in production and manage drift and dashboards
• Collaborate with hardware and full-stack teams to integrate predictions into the product
• Mentor junior engineers on data and ML patterns

Skills

Python

PyTorch

Hugging Face

Ray

Data pipelines

Tools

PostgreSQL

SeaweedFS

Airflow

Dagster

Prefect

MLflow

Job description

About the role

Running 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 do

• Design 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 you

• 5+ 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

Bonus

• Time-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

Stack

Python, PyTorch, AWS (S3, EKS, possibly SageMaker), PostgreSQL, SeaweedFS, Grafana / Mimir. Pipeline orchestration is open (Airflow / Dagster / Prefect).

How we work

• Offices in New York, NY and San 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

Benefits

• Health, 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

Compensation

Base salary: $180,000 – $250,000, adjusted based on location, level, and experience. Total compensation includes substantial equity and the benefits above.

A note on applying

If 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.