5-7 years of experience building and deploying ML models in production
Proficiency in PyTorch, JAX, or similar deep learning frameworks
Strong software engineering skills for complex system maintenance
Capability to tackle open-ended problems and learn new fields quickly
Ability to work autonomously while making significant ownership decisions
BS, MS, or PhD in Computer Science or equivalent experience
Responsibilities
Build and deploy computer vision and multimodal models from prototype to production
Design systems to infer structured attributes and spatial context from images
Train and fine-tune models on diverse real-world image datasets
Develop rigorous evaluation frameworks for vision-language models (VLMs)
Create high-performance image retrieval capabilities using embedding models
Optimize models for efficiency with techniques like distillation and GPU acceleration
Prototype modern algorithms from academic literature and implement in production
Benefits
Comprehensive medical, dental, and vision plans
Flexible spending accounts for medical and dependent care
Employee assistance and wellness programs
13 paid holidays to promote work-life balance
Unlimited paid time off for flexibility
Fully remote work environment for better lifestyle
401(k) retirement plan for financial security
Full Job Description
Senior Machine Learning Engineer
Department: Engineering
Employment Type: Full Time
Location: Remote USA
Compensation: $180,000 - $250,000 / year
Senior Machine Learning Engineer
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, not manage a team. 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
Additional duties and responsibilities as reasonably required by the employee's supervisor or CEO
Requirements and Experience
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)
