Founding ML Engineer, Computer Vision (Object Detection)
At a glance
Mid-level Machine Learning Engineering role at Clera. Remote, United States · full-time · $200,000–$260,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build vision models that distinguish products by brand, model, edition and variant, adapting foundation models to the catalog
- Set accuracy targets by category and score confidence so the product can communicate uncertain matches
- Use model mistakes to guide labeling priorities with the labeling team, while balancing new categories against accuracy gains
- Take models from research into production serving, weighing response time, cost and reliability as usage scales
- Explain model strengths, limits and technical tradeoffs to company stakeholders
What you bring
- At least five years applying computer vision, including taking systems into production at meaningful scale
- You have built recognition for visually similar products, where confusing near-matches is unacceptable
- You have fine-tuned and deployed vision transformers or CNNs in PyTorch or TensorFlow
- You can adapt foundation vision models, assess progress beyond loss metrics, and make serving tradeoffs around latency, cost and reliability
Who this fits
This suits a hands-on engineer ready to set technical direction on an open-ended computer vision problem. The role is hybrid in New York, and visa sponsorship is unavailable.
From the employer
About the Role
Join an early-stage marketplace company as its first machine learning engineer focused on computer vision. You will lead the design and production deployment of fine-grained item identification models, helping users identify products accurately and reliably from photos.
What You'll Do
Design computer vision architecture for identifying items by brand, model, edition, and variant, adapting foundation vision models to a specific catalog.
Define category-level accuracy standards and build calibrated confidence scoring so the product can communicate uncertainty appropriately.
Create feedback loops that use model errors to guide labeling priorities in partnership with the labeling team.
Set priorities between expanding category coverage and improving accuracy in existing categories.
Own the model serving path from research through production, balancing latency, cost, and reliability at scale.
Explain model capabilities, limitations, and technical tradeoffs to company stakeholders.
What We're Looking For
At least 5 years of applied computer vision experience, including shipping systems to production at meaningful scale.
Hands-on experience with fine-grained or instance-level recognition, distinguishing visually similar products where near-misses are not acceptable.
Production experience fine-tuning and deploying vision transformers or CNNs using PyTorch or TensorFlow.
Experience adapting foundation vision models to specific use cases, and designing evaluation frameworks that measure progress beyond loss metrics.
Experience with calibrated prediction confidence and ownership of model serving decisions, including latency, cost, and reliability.
Clear written and verbal communication skills, and comfort leading technical direction on an open-ended problem. Active learning, human-in-the-loop labeling, low-latency APIs, and early-stage startup experience are useful.
Compensation & Benefits
Base salary: $200,000 to $260,000 annually. Visa sponsorship is not available.
Location
Hybrid role based in New York, New York.
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