Multimodal Data Engineer: End-to-End Pipelines + Equity

ABAKA AI; Mountain View (CA); On-site; USD 120,000 - 225,000; Full time

Benefits offered by this job

Equity in Abaka AI

Comprehensive benefits: health, dental

Vision coverage

PTO and flexible work schedule

Job summary

Abaka AI is seeking a Multimodal Data Engineer in the United States to create systems and workflows turning complex data requirements into high‑quality datasets for leading AI teams. You’ll own projects from inception to delivery, defining feasibility, quality standards, and scalable pipelines across data modalities, while coordinating with engineers, annotation teams, and vendors.

This role strengthens our data engineering infrastructure and tooling, demanding hands-on systems work, ownership

Qualifications

• 3+ years of experience in data engineering or building large-scale data systems, with hands-on ownership of production data workflows.
• Experience delivering datasets or data systems end to end, including scope, timeline, quality, cost, and acceptance criteria for external clients or internal model teams.
• Hands-on experience with at least two data modalities, such as text, images, audio, video, or 3D point clouds.
• Experience designing and operating large-scale data pipelines using distributed processing tools such as Spark, Ray, or Flink; orchestration tools such as Airflow, Dagster, or Argo; cloud object storage; and containerized batch compute.
• Working knowledge of the infrastructure behind data pipelines, including cloud permissions, storage layout and lifecycle rules, compute provisioning, orchestration environments, and cost monitoring.
• Experience designing data quality and acceptance processes, including sampling plans, defect categories, measurable quality gates, or model-assisted quality control.
• Familiarity with data privacy and security practices, including access control, anonymization, license and provenance tracking, and encryption.
• Ability to translate ambiguous requirements into actionable technical plans with explicit assumptions, resource needs, risks, and acceptance criteria.
• Strong ownership, sound judgment, and comfort working across engineering and operations in a fast-moving environment.

Responsibilities

• Assess incoming data requirements for feasibility, technical challenges, risks, cost, and delivery timeline.
• Define execution plans, resource needs, quality specifications, and written acceptance criteria before work begins.
• Own technical delivery for assigned projects, from scoping through final handoff.
• Break requirements into tasks with clear owners, priorities, deadlines, and deliverables, and coordinate across engineers, annotation teams, and external vendors.
• Build and improve scalable pipelines for multimodal data sourcing, processing, cleaning, annotation, quality assurance, storage, and delivery.
• Establish quality gates at ingestion and before delivery.
• Define measurable checks, identify unperformable checks, and prevent non-conforming data delivery.
• Improve visibility into project and dataset status with trackers and indexes.
• Help operate infrastructure behind data workflows, including storage, batch compute, resources, orchestration, and internal services.
• Track infrastructure usage and project costs; find efficiency improvements without harming delivery commitments.
• Standardize workflows, automate steps, build tooling, document reusable practices from retrospectives.
• Partner with client-facing teams to clarify data needs and communicate tradeoffs.

Skills

Ownership

Problem solving

Cross-functional collaboration

Communication

Hands-on systems thinking

Tools

Spark

Ray

Flink

Airflow

Dagster

Argo

Cloud object storage

Kubernetes

Job description

Abaka AI is seeking a Multimodal Data Engineer in the United States to create systems and workflows turning complex data requirements into high‑quality datasets for leading AI teams. You’ll own projects from inception to delivery, defining feasibility, quality standards, and scalable pipelines across data modalities, while coordinating with engineers, annotation teams, and vendors.

This role strengthens our data engineering infrastructure and tooling, demanding hands-on systems work, ownership