Middle ML Engineer (Computer Vision)
New
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As founding ML engineer, you'll design computer vision architecture for fine-grained item identification at an early-stage marketplace, building production models that distinguish between similar items and setting accuracy standards. You need at least five years of applied computer vision experience with production deployments, strong fluency in PyTorch or TensorFlow, and hands-on experience fine-tuning vision transformers or CNNs. The role requires owning the full path from model development to production serving, managing tradeoffs between latency and accuracy, and collaborating on feedback loops for continuous improvement. On-site in San Francisco; $200,000 to $260,000 annually.
Written from this posting by Neural Jobs AI. The full description is below.
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.
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.
Annual base salary: $200,000 to $260,000.
On-site in San Francisco, California, United States.
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