# Data Engineer, Machine Learning

**Company:** [Sesame](https://hotfix.jobs/companies/sesame)
**Location:** San Francisco, CA
**Role:** Data Engineering
**Salary:** $170k – $240k/yr
**Experience:** 5+ years
**Skills:** Python, SQL, Airflow, Dagster, Prefect, ETL, ELT, Dataset Versioning, Data Quality, Ray, Spark, Kubernetes, GKE, EKS, Vector Databases
**Posted:** 2026-06-23

> Build and maintain production data pipelines that prepare conversational, voice, and multimodal data for ML model training and evaluation. Partner closely with ML engineers to deliver high-quality, versioned datasets and infrastructure.

## Job Description

## Responsibilities
- Design and build production data pipelines that prepare conversational, voice, and multimodal data for model training and evaluation.
- Partner directly with ML engineers to understand data requirements for new models and experiments, and deliver datasets that meet those needs.
- Build and maintain infrastructure for dataset versioning, lineage tracking, and reproducibility.
- Develop data quality frameworks: schema validation, drift detection, and coverage monitoring.
- Optimise large-scale data processing for cost and performance across cloud infrastructure.
- Build tooling that makes it easy for ML engineers and researchers to discover, explore, and request data independently.
- Define and enforce data governance and privacy standards, particularly around sensitive conversational and voice data.
- Contribute to architecture decisions around the broader data platform.

## Requirements
- 5+ years in data engineering, with meaningful experience supporting ML or AI teams.
- Strong SQL and Python skills.
- Experience building and operating ETL/ELT pipelines at scale using modern data platforms and tooling.
- Experience with workflow orchestration systems such as Airflow, Dagster, or Prefect.
- Hands-on experience with ML data workflows: training data pipelines, dataset versioning, data labeling pipelines, or model evaluation data.
- Solid understanding of how ML teams work and what makes a good training dataset.
- Comfort working with unstructured and semi-structured data — audio, text, JSON logs.
- Strong communication skills.

## Nice-to-Haves
- Vector databases, embedding storage, or feature stores.
- Data from hardware or embedded systems: telemetry, sensors, real-time streams.
- Distributed compute frameworks such as Ray or Spark.
- Kubernetes and managed Kubernetes environments such as GKE or EKS.
- Data privacy frameworks, especially around voice or conversational data.
- Building internal tooling or self-serve data platforms.

## Benefits
- 401(k) max employer match: 3.5% of compensation
- 100% employer-paid health, vision, and dental benefits for you and your dependents
- Unlimited PTO and sick time
- Flexible spending account with employer matching up to $1,650/year (medical FSA)
- Guardian Employee Assistance Program (EAP)
- Competitive stock options

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