Machine Learning Engineer - Remote

Overview

Stack is developing revolutionary AI and advanced autonomous systems designed to enhance safety, reliability, and efficiency of modern operations. Stack's autonomous technology incorporates cutting-edge advancements in artificial intelligence, robotics, machine learning, and cloud technologies, empowering us to create innovative solutions that address the needs and challenges of the dynamic trucking transportation industry. With decades of experience creating and deploying real world systems for demanding environments, the Stack team is dedicated to developing an autonomous solution ecosystem tailored to the trucking industry's unique demands.

In Short

• Experience with both ML Platforms and building ML-based applications (bonus point if you have modeling experience).
• Experience building scalable, reliable infra at a fast-paced environment.
• Ability to work across teams.
• Experience building or using ML infra built for a large number of customer teams.
• A deep understanding of design tradeoffs and ability to articulate those tradeoffs and work with others on getting alignment.
• Experience with building ML models or ML infra in the domains of autonomous vehicles, perception, and decision making (desirable but not required).
• Experience with model training, model optimization, or large data processing pipelines.

Requirements

• Knows how to push the GPU to its limit from Python to CUDA kernel level.
• Built the inference or training loop for a large model (ideally with LLM flavor).
• Shipped ML products (NLP, computer vision, recommender systems, etc.) at scale to make business impact.
• Knows how to build low latency / high throughput batch or stream processing pipelines.
• Knows how to write (readable) high performance C++.
• Prior AV experience.

Benefits

• We are proud to be an equal opportunity workplace.
• We believe that diverse teams produce the best ideas and outcomes.
• We are committed to building a culture of inclusion, entrepreneurship, and innovation across gender, race, age, sexual orientation, religion, disability, and identity.