Software Engineer, ML Infrastructure

Rebar is building the AI operating system for commercial HVAC, Electrical, and Plumbing.

Over the past year our quoting platform has processed tens of thousands of projects across North America and we’re continuing that growth. Our customers include many of the top firms in the industry. Some of these companies are running billion dollar construction projects on workflows that still look like it's 1985.

Construction is 10% of GDP and still massively underserved by software. We are changing that.

We recently raised a $14M Series A from leading construction tech investors and are entering our next phase of growth. We are building a set of AI native products that will define how this industry operates.

The role of the engineer is rapidly changing. We're aware and we're being very intentional of ensuring we adapt with it. We are fostering an engineering culture of growth and development. We strongly emphasize care of craft and winning together. To echo our values, everyone operates like an owner, we find a way, and we win together.

We're hiring a Software Engineer to own and expand the ML infrastructure platform our ML engineers depend on to rapidly iterate, experiment, and ship models. This work will span feature pipelines, training infra, evaluation, deployment, and monitoring. You should be well versed with GPU architecture and performance focused - understanding what is blocking the engineers as well as our MFU. You'll be joining a small group of talented engineers focused on delivering practical, production-ready ML systems in a fast-moving startup context.

The role is ideal for someone who is obsessive over the developer experience of the engineers they support, is interested in training, and meticulous over performance. Our team is still (somewhat) lean and engineers wear many hats. Your purview will span the whole ML lifecycle.

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• You'll help build services that act as the single front door to our ML platform

• Explore and help instrument our active learning pipelines, retrainings, model registry, and more

• Smooth the seams for GPU inference and our cloud stack

• We utilize DataDog for our observability. You should be comfortable extending DD and Modal support to have clear GPU util metrics as well as clean insights into our entire inference flow

• You will work closely with ML engineers to understand their workflows, turn one-off scripts into self-serve platform features, and participate in architecture and roadmap decisions.

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.

We're seeking someone with strong platform-engineering instincts who enjoys turning fragmented workflows into products teams actually want to use. 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.

• 6+ years of experience building production backend systems, with significant time on internal developer platforms, ML platforms, or integration-heavy infrastructure work.

• Strong Python + PyTorch, including profiling and debugging below the model code

• 3+ years of experience with cloud infrastructure (AWS preferred)

• Hands on GPU performance and inference work. Ideally you have optimizations in production: batching, mixed precision, quantization, ONNX Runtime/TensorRT or torch.compile

• A proven track record operating inference at large scale across a range of model types - detection, segmentation, recognition, and LLM/VLM workloads.

• Experience managing a model zoo / model registry - versioning, promotion, and governance of models from experiment to production.

• 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.