Rebar

Senior ML Infrastructure Engineer

New York City · Senior

Sponsorship not specifiedDetected 54 days ago

AWSGCPCloud PlatformsTerraformMachine LearningLLMsElectrical Engineering

About the role

Over the past year, our V1 quoting product has scaled to thousands of quotes completed weekly, doubled revenue in 2026, and gained adoption across many of the top suppliers in North America.
You'll be joining a small, highly capable team focused on delivering practical, production-ready ML systems in a fast-moving startup context.
Make launching a training job, tracking an experiment, or shipping a model feel like one coherent product.

Platform & Developer Experience: Design and build the CLI, SDK, and services that serve as the single front door to our ML platform. Make launching a training job, tracking an experiment, or shipping a model feel like one coherent product.
Own the abstractions for compute orchestration, feature store, model registry, and model deployment.
Observability & Operations: Build cost attribution, usage dashboards, and monitoring across the platform.
You should feel confident designing developer-facing APIs and SDKs, integrating disparate cloud and SaaS services into coherent systems, and obsessing over the experience of the engineers who use what you build.
Platform & Developer Experience: Design and build the CLI, SDK, and services that serve as the single front door to our ML platform.
Rebar is building the next-generation operating system for commercial HVAC, electrical, and plumbing suppliers and subcontractors.
This role is ideal for someone who enjoys designing clean abstractions, integrating disparate systems into coherent platforms, and obsessing over the developer experience of the engineers they support.
Design and build the CLI, SDK, and services that serve as the single front door to our ML platform.
Build cost attribution, usage dashboards, and monitoring across the platform.

Bachelor's degree or higher in Computer Science, Electrical Engineering, or other relevant field - or equivalent industry experience.
3+ years of experience building production backend systems, with significant time on internal developer platforms, ML platforms, or integration-heavy infrastructure work.
Proficiency with infrastructure-as-code (IaC) tooling such as Terraform, AWS CDK, or Pulumi for managing reproducible, version-controlled cloud environments.
Hands-on experience with managed ML inference and serving platforms such as AWS SageMaker and GCP Vertex AI.
A proven track record operating inference at large scale across a range of model types - detection, segmentation, recognition, and LLM/VLM workloads.
This role is a great fit if you have taste in abstractions, opinions about developer experience, and a track record of making ML or data teams meaningfully more productive.

Experience integrating common ML tooling - experiment trackers (W&B, MLflow), feature stores, model serving frameworks - into broader platforms.
Experience with DAG / workflow orchestration frameworks such as Temporal, Prefect, or Apache Airflow.
Built a Backstage-style internal developer portal or comparable internal platform.
Familiarity with GPU compute providers (AWS, Lambda Labs, CoreWeave, RunPod).
Some ML practitioner background - you've trained or deployed models yourself and understand the workflow from the user's side.
Experience with deployment and monitoring pipelines for ML systems.
You'll be at the heart of our fast-paced operations, actively contributing to a culture that values engagement, growth, and teamwork.
2+ years of experience with cloud infrastructure (AWS preferred), including IAM, networking, and cost management.

Fresh off a $14M Series A backed by leading construction tech investors, we're entering our next phase of growth - with AI at the center of everything we build next.
We're looking for a Senior ML Infrastructure Engineer to build the platform our ML engineers depend on to rapidly iterate, experiment, and ship models - spanning feature pipelines, training infrastructure, evaluation, deployment, and monitoring.
Our work spans the full ML lifecycle, and we're building the platform that makes it all hang together.

This listing is sourced directly from Rebar's careers page and normalized into a canonical job model.