Machine Learning Engineer, Computer Vision

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