Multimodal Engineer
Required skills
- Python
- low-level formats
- timing
- data integrity
- batch or GPU inference
- automated QA/QC
- dataset versioning
- failure recovery
- production datasets
Nice to have
- camera calibration
- signal processing
- FFmpeg
- GStreamer
- large-scale dataset search
Role summary
Nori Robotics, a YC S26 startup building affordable humanoid robots, seeks a Multimodal Engineer to own their dataset-processing and quality pipeline. This role involves building pipelines for synchronized video, audio, and sensor data, implementing QA/QC measures, running existing models for analysis, and managing dataset versioning. The ideal candidate has strong Python skills and experience with large-scale media or sensor data pipelines, focusing on data integrity, batch/GPU inference, dataset versioning, and failure recovery. Nice-to-have skills include camera calibration, signal processing, FFmpeg/GStreamer, and large-scale dataset search. This is a primarily in-person role in San Francisco, with remote options available.
Scope: Own the dataset-processing and quality pipeline for synchronized multicamera video, multichannel audio, and sensor recordings. Turn raw sessions into aligned, validated, searchable datasets that researchers can use for model development and evaluation.
What you will work on
• Build decoding, synchronization, calibration, normalization, and metadata pipelines for large video, audio, and sensor datasets.
• Implement QA/QC for corrupt or missing files, dropped frames, audio alignment, synchronization drift, and annotation consistency.
• Run existing models for captioning, tracking, embeddings, and automated quality checks; measure errors and route uncertain outputs for review.
• Version datasets and derived features with source lineage; deliver research-ready exports and improve processing throughput, recovery, and cost.
Requirements
• Strong Python and experience building large-scale media or sensor-data pipelines, including low-level formats, timing, and data integrity.
• Experience with batch or GPU inference, automated QA/QC, dataset versioning, and failure recovery on production datasets. A PhD is not required.
• Nice to have: camera calibration, signal processing, FFmpeg/GStreamer, or large-scale dataset search.
San Francisco, CA. Full time or contract. Primarily in person; remote arrangements are available.
Compensation. Contract: $70-$110 per hour. Full time: $130,000-$200,000 per year. Base pay varies with market location, job-related knowledge, skills, and experience.
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