Machine Learning Engineer

Quest Global•Austin, TX

3d•$100,000 - $105,000

About The Position

Quest Global delivers world-class end-to-end engineering solutions by leveraging our deep industry knowledge and digital expertise. By bringing together technologies and industries, alongside the contributions of diverse individuals and their areas of expertise, we are able to solve problems better, faster. This multi-dimensional approach enables us to solve the most critical and large-scale challenges across the aerospace & defense, automotive, energy, hi-tech, healthcare, medical devices, rail and semiconductor industries. We are looking for humble geniuses, who believe that engineering has the potential to make the impossible possible; innovators, who are not only inspired by technology and innovation, but also perpetually driven to design, develop, and test as a trusted partner for Fortune 500 customers. As a team of remarkably diverse engineers, we recognize that what we are really engineering is a brighter future for us all. If you want to contribute to meaningful work and be part of an organization that truly believes when you win, we all win, and when you fail, we all learn, then we’re eager to hear from you. The achievers and courageous challenge-crushers we seek, have the following characteristics and skills

Requirements

Expert-level proficiency in Python is standard
In-depth knowledge of ML frameworks such as TensorFlow / PyTorch
Solid understanding of probability, statistics, linear algebra, and calculus is essential for algorithm development and model evaluation.
Experience with software architecture, data structures, unit testing, version control (Git), and general software development best practices.
Familiarity with tools for processing data
Experience with cloud services for deploying and managing ML models (AWS).
Experince with Data pipelines, SQL and Snowflake
Strong analytical and problem-solving abilities.
Excellent communication and teamwork skills.
Adaptability and a commitment to continuous learning in a fast-evolving field

Responsibilities

Designing, and Developing ML systems .
Collaborating with data scientists and engineers to select, collect, clean, preprocess, and analyze large datasets for model training and feature engineering.
Time series forecasting
Implementing appropriate ML algorithms and using frameworks like TensorFlow or PyTorch.
Building scalable ML pipelines, deploying models into production environments (often using Docker, Kubernetes, and cloud platforms like AWS), and creating APIs for application integration.
Dashboard and Data Analytics
Analysis to interpret results, and monitor deployed models for performance and potential issues.
Collaboration and Communication: Working with cross-functional teams
Staying updated with the latest advancements in machine learning, deep learning, and AI tooling to incorporate new knowledge into ongoing projects.
Domain: Demand and supply planning