Staff Machine Learning Engineer

Key Skills

Algorithms, Data science techniques, Machine learning techniques, Python Programming, SQL

Job Description

At Shield you’ll be part of an elite team that uses machine learning to develop the best AI pilot. Help us build world-class infrastructure for autonomous behavior, scene understanding and embedded AI. We develop large pipelines for training, evaluating and productizing machine learning models. As a member of our machine learning team you will work with colleagues to develop new models in the areas of reinforcement learning, attention mechanisms and object detection and tracking. You will develop workflows to train and evaluate models and to ensure that they operate at peak performance onboard the different autonomous platform.

What you’ll do:

• create efficient ways to represent and learn in multi-agent 3D environments
• strive to improve neural networks expressive capacity and agent performance
• identify efficient algorithms to train multiple agents driving towards a unified objective
• drive towards scale by leveraging low-fi/high-fi efficient training and other forms of agent pre-training
• Identify efficient algorithms to learn in scenarios of incomplete or imperfect information (e.g., learning how to bluff)
• make progress towards improving rigorous evaluation of agents

Required qualifications:

• Bachelors in computer science, software engineering, machine learning engineering or similar field.
• You have a demonstrated record of working hard, being a trustworthy teammate, holding yourself and others to high standards, and being kind to others.
• Good written and verbal communication skills.

Preferred qualifications:

• Masters degree in machine learning, data science, mathematics, physics or computer science

Salary Range: $145,145 - $231, 855 Annually + Bonus + Benefits + Equity (if applicable)

Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. Information on the benefits offered is here.