Top Transportation Machine Learning Engineer Jobs

Lead applied machine learning research to improve validation of ML components in autonomous vehicles, focusing on simulation-led strategies and automating validation processes.

The Senior ML/AI Software Engineer will build analysis algorithms and maintain evaluation dashboards for GM's autonomy stack performance, leveraging large datasets and statistical methods.

As an AI & ML Engineering Intern, you'll design and build internal AI-powered tools, automate workflows, and contribute to shared AI infrastructure, with a focus on LLMs and machine learning frameworks.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

Design and deploy production-grade ML systems, integrate models into software pipelines, and develop features leveraging ML for optimization and prediction problems.

Lead the development of machine learning models for maritime autonomy, focusing on Radar and EO/IR object detection and semantic segmentation. Collaborate on multi-target tracking and SLAM, and optimize models for edge deployment.

Develop and optimize machine learning models using C++ and CUDA for embedded systems, focusing on latency minimization in AI inference tasks while collaborating with engineers and creating production-ready code.

The Algorithms Scientist at Lyft Ads will design, develop, and deploy machine learning models for ad relevance and optimization, collaborating across teams to enhance performance and revenue growth.

As an Algorithms Scientist, design and deploy machine learning models to optimize Lyft Ads operations, collaborating across teams to enhance ad performance and efficiency.

As a Mapping Software Intern, you will design algorithms for machine perception, develop ML models, and collaborate with cross-functional teams on mapping initiatives.

As a Machine Learning Engineer at Shipwell, you'll build and scale AI-powered logistics solutions, maintaining data pipelines and ML infrastructure across the organization.

Translate multi-modal AI research into 0-1 products: design and run ML experiments, build scalable training and evaluation infrastructure, implement MLOps and data pipelines, deploy models, and use AI code tooling to move experiments to production MVPs.