Senior GenAI Data Engineer: Multimodal & Synthetic Data

Workman Labs; San Mateo (CA); Hybrid; USD 243,000 - 295,000; Full time

Job summary

Workman Labs in San Mateo, CA, is seeking a Senior Machine Learning Engineer to build the data flywheel for GenAI foundations like VideoGen and 3DGen. You will architect petabyte-scale data pipelines, synthetic data generation, evaluation frameworks, and inference APIs in a hybrid work setting.

Ideal candidates have 8+ years in data systems engineering, strong Python, and experience with Spark, Ray, Kubeflow, and S3. Collaboration with researchers and MLOps familiarity are a plus.

Qualifications

• 8+ years as a research-focused data systems engineer.
• Experience building scalable ML data pipelines for batch and real-time.
• Strong Python for automation and tooling.
• Experience with cloud data platforms and distributed processing (Spark, Ray, Kubeflow, S3).
• Nice to have: MLOps tools and game dev/3D data tooling.

Responsibilities

• Architect and maintain automated pipelines for ingestion, cleaning, and pre-processing of multi-modal datasets (video, 3D) spanning petabytes of data.
• Leverage image and video generation models to scale multi-modal synthetic datasets.
• Partner with research teams to create training data and synthetic data pipelines.
• Build and own evaluation with metrics and human-in-the-loop interfaces.
• Design and optimize high-throughput, low-latency inference APIs.
• Participate in literature reviews to identify optimizations in generative modeling.
• Implement monitoring for pipeline health and optimize data loading for GPU efficiency.

Skills

Python programming

Data pipelines

MLOps

Cloud data platforms

Big data

Education

Bachelor's degree or equivalent in Computer Science / Computer Engineering

Tools

Spark

Ray

Kubeflow

S3

C++

Job description

Workman Labs in San Mateo, CA, is seeking a Senior Machine Learning Engineer to build the data flywheel for GenAI foundations like VideoGen and 3DGen. You will architect petabyte-scale data pipelines, synthetic data generation, evaluation frameworks, and inference APIs in a hybrid work setting.

Ideal candidates have 8+ years in data systems engineering, strong Python, and experience with Spark, Ray, Kubeflow, and S3. Collaboration with researchers and MLOps familiarity are a plus.