Data Engineer, Machine Learning
$170k - $280k • Remote • San Francisco • FullTime
Posted 3mo ago
About the job
Sesame is building a new kind of computer focused on making voice agents part of our daily lives, aiming for a future where computers are lifelike and can collaborate with us naturally. We are seeking a Data Engineer to construct and manage the data pipelines essential for training, evaluating, and deploying Sesame's AI models. This role involves close collaboration with machine learning engineers and researchers, ensuring they have access to high-quality, well-structured data derived from rich and complex sources like conversations, voice, and sensor signals. You will design systems to transform raw, unstructured, multimodal data into clean, versioned, and documented datasets, playing a critical role in accelerating the entire model development lifecycle.
Responsibilities
- Design and build production data pipelines for conversational, voice, and multimodal data preparation for model training and evaluation.
- Collaborate with ML engineers to understand data requirements and deliver datasets for new models and experiments.
- Build and maintain infrastructure for dataset versioning, lineage tracking, and reproducibility.
- Develop data quality frameworks for schema validation, drift detection, and coverage monitoring.
- Optimize large-scale data processing for cost and performance on cloud infrastructure.
- Build tooling to enable ML engineers and researchers to independently discover, explore, and request data.
- Define and enforce data governance and privacy standards for sensitive data.
- Contribute to architecture decisions for Sesame's broader data platform.
Requirements
- 5+ years of data engineering experience, with significant experience supporting ML or AI teams.
- Strong proficiency in SQL and Python.
- Experience building and operating scalable ETL/ELT pipelines with modern data platforms.
- Experience with workflow orchestration systems like Airflow, Dagster, or Prefect.
- Hands-on experience with ML data workflows, including training data pipelines, dataset versioning, data labeling, or model evaluation data.
- Understanding of ML team workflows and the importance of data quality for model performance.
- Comfort working with unstructured and semi-structured data (audio, text, JSON logs).
- Strong communication skills to bridge data systems and model requirements.
Benefits
- 401(k) with a 3.5% employer match.
- 100% employer-paid health, vision, and dental benefits for employees and dependents.
- Unlimited PTO and sick time.
- Flexible spending account with up to $1,650/year employer matching for medical expenses.
- Guardian Employee Assistance Program (EAP).
- Competitive stock options.