Remote ML Engineer (Computer Vision) + Equity Options

Surveily; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Founders collaboration

Fully remote work

Fitness benefits

Health benefits

Employee stock options

Gaming laptop

Company off-sites

Job summary

Surveily is hiring a Machine Learning Engineer to build production-ready computer vision features that solve real customer problems. You will own the full ML lifecycle—from data collection and annotation to training, deployment, and continuous improvement—working across data, modeling, software engineering, deployment, and product integration.

You will contribute to reusable tooling, improve developer workflows, and write production-grade code, collaborating closely with software engineers and

Qualifications

• 3–5 years of commercial experience building machine learning systems.
• Strong Python software engineering skills and production-grade code.
• Experience with PyTorch and deploying ML models to production.
• Solid understanding of ML lifecycle, data quality, evaluation, and deployment.

Responsibilities

• Design production-ready computer vision features from concept to deployment.
• Translate requirements into practical ML solutions.
• Work across data collection, annotation, dataset management, experiments, training, deployment, and monitoring.
• Build robust Python tooling and shared libraries to boost team productivity.
• Develop maintainable, well-tested production code.
• Collaborate with software engineers and product stakeholders to deliver value.
• Improve model quality through data-driven experimentation and reproducibility.

Skills

Python

PyTorch

Computer Vision

ML fundamentals

Data management

Experimentation

Tools

Docker

Kubernetes

Cloud platforms (AWS/GCP)

ML tooling

Job description

Surveily is hiring a Machine Learning Engineer to build production-ready computer vision features that solve real customer problems. You will own the full ML lifecycle—from data collection and annotation to training, deployment, and continuous improvement—working across data, modeling, software engineering, deployment, and product integration.

You will contribute to reusable tooling, improve developer workflows, and write production-grade code, collaborating closely with software engineers and