Principal Tech Lead Manager - VLM/LLM for Large Scale Data Mining

Position Overview

• Location Type: Not Specified
• Job Type: Not Specified
• Salary: $175,000 - $281,100 USD

Motional is seeking a highly skilled and motivated Principal Technical Lead Manager to lead large-scale data mining efforts utilizing cutting-edge Vision Language Models (VLM) and Large Language Models (LLM). The ideal candidate will have a strong background in machine learning, data engineering, and leadership, with experience in developing and deploying advanced AI solutions for data analysis and extraction.

At Motional, data plays a critical role in fueling our ML-centered autonomous driving vehicle. Our robo-taxi fleet collects petabytes of data on the road every day – the Data Mining team is mining & filtering the massive influx of fleet data by developing billion-scale data workflows and state-of-the-art mining algorithms. Through our mining and learning frameworks, we continuously improve the on-road performance of ML products for perception, prediction & planning with every mile driven. We mine for model errors, anomalies, rare objects & long-tail driving scenarios across millions of driving hours – these are used for laser-focused ML model training and continuous edge case validation. We are looking for an engineer to spearhead new mining strategies & workflows and help deliver high-quality data that improve our core ML products.

Responsibilities

• Lead a team of engineers and researchers in developing data products utilizing foundation models such as multi-modal encoding models (CLIP-style models), vision language models (VLMs) and large language models (LLMs) and adapt them to the autonomous driving domain via pre-training, fine-tuning and prompt optimization.
• Drive innovation by researching and experimenting with new VLM/LLM techniques and technologies.
• Own large-scale mining workflows that surface rare objects, model errors & long-tail events.
• Build high-quality datasets to improve ML products through training & edge case validation.
• Provide statistical depth on model performance & generalization through rigorous error analysis across complex driving scenarios.
• Provide technical leadership and mentorship to the team, fostering a culture of collaboration and continuous improvement.
• Manage project timelines, resources, and deliverables to ensure successful project execution.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• Extensive experience in machine learning, data mining, and natural language processing.
• Proven experience in developing and deploying VLM/LLM models.
• Strong programming skills in Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.
• Experience with large-scale data processing and distributed computing technologies.
• Excellent leadership, communication, and problem-solving skills.

Bonus Points

• Ph.D. in Computer Science or a related field.
• Experience with cloud platforms such as AWS, Google Cloud, or Azure.
• Experience with machine learning in the autonomous driving domain.
• Publications or contributions to the AI/ML community.

Company Information

Motional is a driverless technology company. Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more.

Note on Salary: The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.