Senior Software Engineer, Computer Vision

The healthcare industry overspends on its supply chain by over $25B each year — the result of fragmented data, inefficient workflows, and wasted supplies. Clarium is fixing that. Our AI-powered platform, Astra OS, gives hospitals end-to-end visibility into their supply chain operations, automating workflows and surfacing actionable insights so supply chain teams can focus on what matters most: patient care. We're trusted by some of the world's leading health systems, including Yale New Haven Health, Stanford, Geisinger, Cleveland Clinic, and Kaiser Permanente.

Founded in 2020, Clarium has raised $43M in total funding. Our Series A was led by Northzone, with participation from General Catalyst, AlleyCorp, Kaiser Permanente Ventures, Texas Medical Center Ventures, and 1984 Ventures.

Clarium builds computer vision pipelines that extract structured data from clinical images under real-world conditions — variable lighting, uncontrolled image quality, and zero tolerance for silent errors. This role owns those pipelines end-to-end: improving accuracy, hardening reliability, and extending them to new use cases.

The work sits at the intersection of AI API orchestration, image processing, and production Python backend engineering. You'll be building systems that combine frontier multimodal AI APIs with deterministic decoders to produce auditable, accurate results that clinical workflows depend on. This is not a research role — the systems you build have direct patient safety implications, and getting it right matters.

One important note on scope: this role does not involve training or fine-tuning models, MLOps infrastructure, or classical ML experimentation. If your background is in building production systems that orchestrate AI APIs and extract structured data reliably — rather than training the models themselves — this is a strong fit.

• Build and improve multi-stage CV pipelines spanning object detection, multimodal LLM extraction, machine-readable code decoding, and multi-source reconciliation

• Own pipeline accuracy — instrument field-level metrics, diagnose failure modes, and drive improvements through prompt engineering, preprocessing strategy, and reconciliation logic

• Write and maintain structured prompting protocols for multimodal models, including systematic extraction sequences, confidence calibration, and graceful handling of ambiguous inputs

• Design persistence schemas and audit data models that make every extraction independently reviewable

• Maintain and extend the async Python backend services that surface pipeline results to downstream clinical workflows

• Production experience building systems on top of multimodal LLM APIs — effective structured-output prompts, schema validation, retry handling, and fallback design

• Comfort with image preprocessing techniques: contrast normalization, thresholding, rotation, compression

• Experience with machine-readable code decoding (1D/2D barcodes, QR codes, or similar) and the preprocessing strategies that improve success rates

• Strong async Python: FastAPI, Pydantic v2, asyncpg, PostgreSQL

• Reliability-first mindset — you build pipelines that produce auditable output even when individual stages fail

Nice to Have

• Experience with open-vocabulary or zero-shot object detection as a pipeline component

• OCR or document understanding pipelines applied to structured data extraction

• Durable workflow orchestration experience (Temporal, Prefect, Airflow, or similar)

Need to Know: Python · FastAPI · Pydantic v2 · PostgreSQL · Multimodal LLM APIs · Image preprocessing · Barcode / QR decoding

Nice to Know: Zero-shot object detection · OCR pipelines · Temporal · Prefect · Airflow

• Incentive Stock Options proportionate to your salary

• Fully remote — we're a distributed team across multiple time zones

• Unlimited PTO

• Top-tier health, vision, and dental benefits

• The opportunity to build on a strong foundational team with deep data and engineering roots at a stage where your work genuinely shapes the product