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
- Set the computer vision approach for identifying specific brands, models, editions and variants across catalog items
- Adapt foundation vision models to catalog identification and set category-specific accuracy targets with calibrated confidence scores
- Use model mistakes to guide labeling priorities, working with the labeling team to improve the data
- Choose where to expand category coverage versus improving accuracy in categories already supported
- Take models from research into production, making decisions about serving speed, cost and reliability
What you bring
- At least five years applying computer vision, including production systems delivered at meaningful scale
- You have built fine-grained or instance-level recognition that distinguishes visually similar items
- You have fine-tuned and deployed vision transformers or CNNs in PyTorch or TensorFlow
- You can evaluate model improvements beyond training loss and have handled confidence calibration and serving tradeoffs
Who this fits
This founding role suits someone ready to set technical direction and lead an open-ended computer vision effort, while explaining model tradeoffs to technical and non-technical colleagues. It is hybrid and based in New York, and visa sponsorship is unavailable.
From the employer
About the Role
As the founding machine learning engineer, you will set the technical direction for fine-grained visual item identification at an internet marketplace platform. You will build production models that identify specific brands, models, editions, and variants, helping users make informed decisions with confidence.
What You'll Do
Design and own computer vision architecture, adapting foundation vision models to specialized catalog identification.
Define category-level accuracy standards and develop calibrated confidence scoring so the product can communicate uncertainty.
Build feedback loops that use model errors to guide labeling priorities in partnership with the labeling team.
Balance broader category coverage against deeper accuracy in existing categories.
Own the path from model research to production serving, including latency, cost, and reliability.
Explain model capabilities and tradeoffs to technical and non-technical stakeholders.
What We're Looking For
At least 5 years of applied computer vision experience, including production systems delivered at meaningful scale.
Hands-on experience with fine-grained or instance-level recognition, distinguishing between visually similar items where near-misses are not sufficient.
Production experience fine-tuning and deploying vision transformers or CNNs with PyTorch or TensorFlow.
Experience adapting foundation vision models to specific use cases or catalogs.
Ability to design and run evaluation frameworks that measure model improvements beyond training loss.
Experience with calibrated confidence scoring and ownership of model serving decisions, including latency, cost, and reliability.
Strong written and verbal communication, and comfort providing technical leadership on an open-ended problem.
Compensation & Benefits
Base salary: $200,000 to $260,000 annually. Visa sponsorship is not available.
Location
Hybrid role based in New York, New York.