Part TimeTemporaryFreelanceEN
Location: Full remote
Schedule: Freelance – Hourly / On-demand | 3 months
This partnert is looking to add a freelance professional to support projects related to computer vision, traffic analytics, and machine learning applied to real-world data.
This is a flexible, project-based opportunity, ideal for someone who works autonomously, has a hands-on mindset, and enjoys solving meaningful technical challenges with visible business impact.
The selected candidate will work directly with the CTO in the United States and collaborate with cloud engineering, product, and development teams.
Model Development & Deployment
Design, train, evaluate, and deploy machine learning models for real-time and near–real-time applications.
Partner with the Senior Cloud Engineer to deploy models within cloud pipelines.
Translate physical world into actionable metrics.
Data Pipeline Collaboration
Collaborate on the design and optimization of data pipelines used for model training, inference, and monitoring.
Define data requirements, feature schemas, and validation checks to ensure high-quality inputs.
Work with edge and cloud data sources to balance latency, cost, and performance.
Production ML & Monitoring
Establish metrics for model performance, data drift, and system health.
Analyze the impact of new features and model changes in production environments.
Continuously improve model accuracy, robustness, and efficiency based on real-world feedback.
Cross-Functional Collaboration
Work closely with cloud, perception, and UI engineering teams to integrate ML outputs into customer-facing products.
Communicate findings, tradeoffs, and recommendations to technical and non-technical stakeholders.
Applied Machine Learning
3–6 years of experience building and deploying machine learning models in production environments.
Strong foundation in supervised and/or unsupervised learning, time-series, or real-time inference use cases.
Real-Time & Scalable ML Systems
Experience supporting low-latency or streaming ML workloads.
Cloud & Data Ecosystem
Hands-on experience working in AWS (or similar cloud environments) alongside cloud engineers.
Comfort interacting with data stored in PostgreSQL, Snowflake, or similar systems.
Programming & Tooling
Strong proficiency in Python and common ML libraries (e.g., PyTorch, DeepStream, scikit-learn).
Familiarity with ML lifecycle tooling (experiment tracking, model versioning, CI/CD for ML).
Intermediate English level (written and spoken).
