Founding ML Engineer, Computer Vision (Object Detection)

San Franciscofull timeOn Site

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mid

$200k–$260k

H1B: no

Clera Founding ML Engineer responsible for designing computer vision architecture for fine-grained item identification and building production models to support customer decisions. Owns model development to production serving balancing latency, cost, and reliability.

Responsibilities

As the founding machine learning engineer at an early-stage internet marketplace company, you will set the technical direction for visual item identification. You will build production models that distinguish between closely related items and provide reliable predictions that support customer decisions.
Design computer vision architecture for fine-grained item identification, starting with foundation vision models and adapting them to the marketplace catalog.
Set category-specific accuracy standards and build calibrated confidence scoring so the product can communicate uncertainty.
Develop feedback loops that use model errors to prioritize future labeling in collaboration with the labeling team.
Guide tradeoffs between expanding category coverage and improving accuracy in existing categories.
Own the path from model development to production serving, balancing latency, cost, and reliability.
Explain model capabilities and limitations to technical and non-technical stakeholders.

Qualification

Experience with active learning

Required

At least 5 years of applied computer vision experience, including shipping a system to production at meaningful scale.
Hands-on experience with fine-grained or instance-level classification, where distinguishing similar items matters.
Strong fluency in PyTorch or TensorFlow and practical experience fine-tuning and deploying vision transformers or CNNs.
Experience designing and running vision model evaluations that measure improvement beyond loss metrics.
Experience owning model serving decisions, including latency, cost, reliability, and calibrated confidence scoring.
Comfort taking technical ownership of an open-ended problem and communicating tradeoffs clearly.
Experience with active learning, human-in-the-loop labeling, low-latency model APIs, or an early-stage startup is a plus.