Our team is looking for a Machine Learning Engineer to help design, prototype, deploy, and improve machine learning systems for road and infrastructure intelligence. The role is primarily focused on computer vision, especially object detection and segmentation, but may also include broader ML work depending on project needs.

This is a hybrid R&D and production engineering role. You will work across the ML lifecycle, including dataset curation, experimentation, model training, evaluation, optimization, deployment, monitoring, and iteration in production. The role also includes system design, ML pipeline design and implementation, and close collaboration with product and engineering teams to integrate ML solutions into real workflows and products.

We are looking for someone who can move quickly from idea to prototype and from prototype to production, while maintaining strong technical judgment around model quality, performance, and reliability.

How to Apply

We only accept applicants via email. Send your resume and GitHub or portfolio link to info@pavepal.ai with the subject line “ML Engineer Application – [Your Name]”.

Minimum Qualifications:

• Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field
• 2–3+ years of professional experience building and shipping ML systems, or 1+ year for candidates with strong research experience and a Master’s or PhD
• Strong programming skills in Python
• PyTorch is required
• Strong foundation in machine learning, including training dynamics, model evaluation, error analysis, experimental design, and generalization
• Practical experience with object detection, segmentation, and/or image classification
• Familiarity and hands-on experience with transformer-based models
• Experience with dataset curation, augmentation, and evaluation set development
• Strong understanding of common training issues such as overfitting, underfitting, class imbalance, noisy labels, and domain shift
• Experience optimizing models for production, including latency, throughput, and resource constraints
• Experience supporting or deploying inference systems for real-time and/or batch processing workflows
• Familiarity with production monitoring, model performance tracking, and iterative improvement
• Experience with AWS and/or GCP
• Familiarity with Git, CI/CD workflows, and containerized development/deployment
• Familiarity with tools and ecosystems such as Hugging Face, ONNX, and related deployment workflows

Responsibilities

• Strong ML fundamentals, not just familiarity with frameworks
• Fast prototyping ability with strong execution
• Someone comfortable working across both research-style exploration and production deployment
• Ability to design systems and pipelines, not just train models in isolation
• A collaborative engineer who works well with product, backend, and other technical teams
• Clear communicator with strong problem-solving skills
• Bonus: experience with geospatial computation or edge / on-device ML