Machine Learning Engineer (Computer Vision + MLOps)

Company: Self Inspection
Location: Remote (US / Europe preferred)
Experience: 3–5 years

About Us

Self Inspection is building an AI-powered platform that transforms how vehicle inspections are performed. Using computer vision and machine learning, we automate damage detection, streamline workflows, and deliver high-quality inspection reports for enterprises.

We are a fast-growing startup with a strong technical team, solving real-world problems at scale.

Role Overview

We are looking for a Machine Learning Engineer with strong experience in Computer Vision and MLOps to help us improve and scale our damage detection models.

You will work on designing, training, deploying, and optimizing ML models that power our inspection platform — from experimentation to production.

What You’ll Do

• Build and improve computer vision models for vehicle damage detection (scratches, dents, cracks, etc.)
• Work with annotated image datasets and optimize model performance (precision/recall, latency)
• Design and maintain ML pipelines for training, evaluation, and deployment
• Implement and manage MLOps workflows (CI/CD for ML, model versioning, monitoring)
• Deploy models to production (cloud or edge environments)
• Collaborate with backend engineers to integrate ML services into APIs
• Continuously improve model accuracy using real-world feedback and data

What We’re Looking For

Required:

• 3–5 years of experience in Machine Learning or Computer Vision
• Strong experience with Python and ML frameworks (e.g., PyTorch, TensorFlow)
• Hands-on experience with object detection models (e.g., YOLO, Faster R-CNN)
• Experience building and deploying ML models in production
• Experience with MLOps tools and practices (model versioning, pipelines, monitoring)
• Solid understanding of data pipelines and dataset management

Preferred:

• Experience with cloud platforms (AWS preferred)
• Experience with Docker, Kubernetes, or serverless ML deployments
• Familiarity with tools like MLflow, Airflow, or Kubeflow
• Experience working with image annotation pipelines
• Startup experience or ability to work in a fast-paced environment

Tech Stack

• Python, PyTorch / TensorFlow
• Computer Vision (YOLO and similar models)
• AWS (S3, Lambda, EC2, etc.)
• Docker, Kubernetes
• PostgreSQL
• Backend: NestJS

Why Join Us

• Work on a real-world AI product with immediate impact
• Own features end-to-end — from model to production
• Small, high-performing team with strong engineering culture
• Opportunity to shape the future of AI in automotive inspections
• Flexible work environment

Compensation & Benefits

• Competitive salary
• Flexible schedule
• Paid time off and holidays
• Opportunity for growth in a fast-scaling startup