Senior Machine Learning Engineer

Clearview AI; United States; On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Medical, Dental, Vision

STD and LTD Plans

Job summary

Clearview AI is seeking a highly technical individual contributor to advance computer vision and ML capabilities. This is a hands-on role for a research-minded engineer who wants to build and ship models that reason about real-world context behind imagery.

Much of the work focuses on large-scale visual understanding and extracting structured signals from images. You will design scalable systems, train and deploy models, work with vision-language models (VLMs), and build rigorous evaluation

Qualifications

• Experience building, training, evaluating, and deploying ML models in production.
• Strong experience using PyTorch, JAX, or other deep learning frameworks to develop and optimize models.
• Strong software engineering ability to build and maintain complex systems and work with large-scale datasets.

Responsibilities

• Build, train, evaluate, and deploy computer vision and multimodal models, taking them from prototype to production.
• Design systems that infer structured attributes and spatial context from imagery.
• Train and fine-tune models on large real-world image datasets and build data pipelines to curate and label data at scale.
• Work with vision-language models and build evaluation frameworks to measure accuracy.

Skills

ML model dev

PyTorch/JAX

Software engineering

Ownership

Autonomy

Education

BS/MS/PhD in CS or related field

Job description

Position Summary:

We are hiring a highly technical individual contributor to push the limits of our computer vision and machine learning capabilities. This is a high-impact, hands-on role for a research-minded engineer who wants to build and ship models. Much of the work involves large-scale visual understanding, extracting structured signals from imagery and reasoning about the real-world context behind a photograph, but we care more about deep ML/CV ability than any one problem area and welcome strong generalists.

Responsibilities:

• Build, train, evaluate, and deploy computer vision and multimodal models, taking them from early prototype through to production
• Design systems that infer structured attributes and spatial context from imagery, combining learned models with geometric and heuristic reasoning
• Train and fine-tune models on large, diverse real-world image datasets, and build the pipelines to curate and label that data at scale
• Work with vision-language models (VLMs) and build rigorous evaluation frameworks to measure their accuracy on our tasks
• Develop and benchmark high-performance image retrieval capabilities with embedding models and vector indexing strategies
• Optimize models for inference latency and throughput using techniques like distillation, quantization, and GPU acceleration
• Read current research, prototype novel algorithms from academic literature, and turn promising ideas into reliable production code
• Implement efficient, scalable data pipelines and inference infrastructure
• Develop high-performance tooling in ML and data engineering

Requirements:

• Experience building, training, evaluating, and deploying ML models in production
• Strong experience using PyTorch, JAX, or other deep learning frameworks to develop and optimize models
• Strong software engineering ability to build and maintain complex systems and work with large-scale datasets
• Ability to solve open-ended problems and quickly learn new domains
• Comfort operating with significant ownership and autonomy, making pragmatic trade-offs between model sophistication, velocity, inference and business constraints
• BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience

Nice to have:

• Experience inferring structured, real-world attributes from images
• Experience training models on large-scale, real-world image datasets
• Familiarity with vision-language models (VLMs)
• Ability to digest academic literature, prototype novel algorithms, and bridge the gap between research and production code
• Experience building LLM or VLM pipelines and the evaluation frameworks to measure their performance
• Experience in an ML role at a growth-stage startup
• Publications in major ML or computer vision conferences (e.g., CVPR, ICML, ICCV, WACV)
• Medical, Dental, Vision, STD and LTD Plans