Computer Vision Engineer

Harrison Clarke; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Harrison Clarke in San Francisco is seeking a Computer Vision Engineer to design, train, and deploy large-scale vision models. You will own the end-to-end ML lifecycle from data prep to production, building scalable inference services and ML pipelines.

The role requires 3+ years of production ML experience or a PhD in related fields, with strong PyTorch expertise and a background in diffusion/GAN/transformer-based vision models. Collaboration with research and product teams is essential.

Qualifications

• 3+ years of experience building and deploying ML systems in production.
• Strong software engineering experience with applied machine learning.
• Experience training large neural networks using PyTorch (distributed).
• Experience building production ML infrastructure, including model serving and data pipelines.
• Deep understanding of diffusion models, GANs, or transformer-based vision architectures.
• Experience with multimodal learning or computer vision systems.

Responsibilities

• Designing, training, and improving large-scale computer vision and deep learning models.
• Owning the complete machine learning lifecycle from data preparation through production deployment and continuous improvement.
• Building scalable inference services and distributed ML pipelines.
• Developing evaluation frameworks, monitoring systems, and automated retraining workflows.
• Creating synthetic datasets and large-scale data processing infrastructure.
• Improving model accuracy, efficiency, robustness, and reliability through experimentation.
• Working closely with engineering and product teams to bring research into production.
• Contributing technical documentation, research publications, or engineering blogs where appropriate.

Skills

Multimodal learning

Production ML

Distributed training

Software engineering

Large CV models

Education

PhD in Computer Vision or related

Tools

PyTorch

Job description

We\'re partnering with a well-funded early-stage AI startup looking to hire a Computer Vision Engineer to help build next-generation multimodal machine learning systems. This is an opportunity to join a small, highly technical team where engineers have significant ownership over both the research and production lifecycle of the models they build.

This role is ideal for someone who enjoys taking models from experimentation through deployment, working across data, infrastructure, training, evaluation, and production systems. You\'ll help solve technically challenging problems in a fast-moving environment where engineering quality and real-world impact are equally important.

What You\'ll Be Working On

• Designing, training, and improving large-scale computer vision and deep learning models
• Owning the complete machine learning lifecycle from data preparation through production deployment and continuous improvement
• Building scalable inference services and distributed ML pipelines
• Developing evaluation frameworks, monitoring systems, and automated retraining workflows
• Creating synthetic datasets and large-scale data processing infrastructure
• Improving model accuracy, efficiency, robustness, and reliability through experimentation
• Working closely with engineering and product teams to bring research into production
• Contributing technical documentation, research publications, or engineering blogs where appropriate

We\'re Looking For Someone Who Has

• 3+ years of experience building and deploying machine learning systems in production, or a PhD in Computer Vision, Machine Learning, Artificial Intelligence, or a related discipline
• Strong software engineering experience alongside applied machine learning
• Experience training large neural networks using PyTorch, including distributed training
• Experience building production ML infrastructure, including model serving, data pipelines, and scalable deployment systems
• Deep understanding of modern generative image models such as diffusion models, GANs, or transformer-based vision architectures
• Experience working with multimodal learning or computer vision systems
• Familiarity with recent developments in large language models and foundation models

Nice to Have

• Experience with MLOps, model deployment, observability, or ML platform engineering
• Background in image analysis, media authenticity, or visual reasoning
• Experience with synthetic data generation or large-scale data pipelines
• Knowledge of adversarial machine learning, model robustness, or interpretability
• Experience building distributed AI systems that operate reliably at production scale

Why Join

• Small, highly technical team with meaningful ownership and autonomy
• Opportunity to work on challenging machine learning problems with real-world impact
• Research and engineering are closely connected, allowing ideas to move quickly into production
• Significant influence over technical direction, architecture, and model development
• Work alongside experienced researchers and engineers building state-of-the-art AI systems