Principal Machine Learning Engineer, Data Mining

Job description

Job responsibilities

• Set the Technical Direction for Data Mining: Define the multi-year ML roadmap for our multimodal data-mining platform (Omnitag).
• Build the Data Flywheel: Own the full lifecycle of our ML data pipelines to close the active-learning loop. You will align massive multimodal teacher models and distill them into efficient student models for…
• Architect Massive-Scale Systems & Tackle the Hardest Challenges: Design the architecture for applying billion-parameter models to real-world AV logs.
• Champion Cross-Functional Initiatives: Act as the technical bridge between Data Mining, Autonomy, and Infrastructure.
• Lead and Grow a High-Performing Pod: Serve as a hands-on player-coach, directly guiding up to 3 engineers. Delegate ownership, coach your team to deliver state-of-the-art implementations, and advocate for tooling and…

Job requirements

• BS in Computer Science, Machine Learning, or a related field, or equivalent professional experience.
• 12+ years of hands-on machine learning engineering experience, with a proven track record of owning the end-to-end development cycle of enterprise-scale ML systems, from experiment design through infrastructure,…
• Exceptional technical maturity, with a track record of building and shipping impact that is innovative, broad in reach (affecting many teams or orgs), and pays future dividends for the company.
• Proven ability to solve technical problems that few others can, the person others seek out for complex ML infrastructure, deep learning architectures, and production optimization.
• Deep experience training large-scale models from the ground up, including distributed training across multi-node GPU clusters, data pipeline and curation strategy, scaling and evaluation methodology, and the practical…
• Demonstrated ownership of a technical roadmap for a team or problem area, including balancing competing objectives (impact, quality, engineering time, compute cost) and making deliberate tradeoffs between long-term…
• Ability to create clarity from ambiguous, complex problems, decomposing them concisely, defining objectives, and establishing measures of progress for yourself and your team.
• Deep, generalist ML expertise spanning massive-scale model training, hardware-optimized deployment, and production ML orchestration in cloud environments (AWS, GCP, or Azure).