About the Role

As a Senior/Staff Software Engineer, you will be involved and responsible for full ownership and driving key machine learning platform initiatives to improve the life cycle of machine learning including training data management, training, and hyperparameter tuning systems, ML inference microservices, feature, and model drift monitoring, and cost reduction efforts towards delivering product features and value to the KeepTruckin customers.  

We are looking for strong engineers to grow our ML Platform team. ML Platform team is responsible for driving KeepTruckin's ML. The team works in three areas:

• Building shared ML platform for AI/ML Inference and training
• Working closely with the Computer Vision team on models generated from our Dashcam data
• Working closely with the Data Science team on other modeling efforts using the rest of our IoT and application data. 

Learn more about our work culture and team members on this profile of our team here.

You are excited about this opportunity because you will have:

• Opportunity to drive the new product initiatives within KT.
• Opportunity to learn and contribute to computer vision-enabled safety products
• Opportunity to learn and grow in machine learning technologies.
• Opportunity to make an impact across hardware and software technologies.

What You’ll Do: 

• Leading and driving from requirements, scoping, design, development, deployment of machine learning systems.
• Scaling up models and tune parameters to improve system performance.
• Building software that improves training and inference times, cost of running inference in production, rate of experimentation, reducing iteration time, and helps you and the team make better decisions about how and what we build and try next.
• Communicate effectively across multiple teams and projects.
• Ability to learn and adapt quickly to new technologies.
  
• Self-starter and someone who takes and drives initiatives.
  

What we are looking for:

• Ability to train and debug machine learning systems: Defining metrics and datasets, performing error analysis, training models in a modern ML framework (such as TensorFlow, PyTorch, Keras, etc.)
• Familiarity with current ML literature and the common types of models and layers, etc.
• Strong background in Python development with Linux. You write clean, correct code while iterating on experiments in Python. Ability to understand and contribute to Python-based software is a plus.
• Experience and understanding of the infrastructure e.g. K8s, CI/CD, Docker, and others.
• Excellent automation, debugging, and troubleshooting skills.
• Good understanding of operations and building distributed systems at scale.
• Good understanding and experience in building data and machine learning systems.