Multimodal Data Engineer

Abaka AI is built on one mission: to be the world’s most trusted data partner for AI companies. More than 1,000 industry leaders across Generative AI, Embodied AI, and Automotive AI rely on us to power their data pipelines. With our headquarters in Silicon Valley and teams in Paris, Singapore, and Tokyo, we support global partners with fast, reliable, and scalable data solutions.

Our offerings include a diverse catalog of off-the-shelf datasets spanning image, video, multimodal, reasoning, 3D, and more, as well as comprehensive data collection and annotation services. Whether teams need raw data, curated datasets, or full-cycle data engineering, Abaka AI provides the foundation for building high-performance AI systems.

About the Role

We’re hiring a Multimodal Data Engineer in the United States to build the systems and workflows that turn complex data requirements into high-quality datasets for some of the world’s most advanced AI teams.

You’ll own projects from initial requirements through final delivery: assessing feasibility, defining quality standards, building scalable pipelines, coordinating technical work, and ensuring datasets meet agreed specifications. The work spans multiple modalities and every stage of the data lifecycle, including sourcing, processing, annotation, quality control, storage, and delivery.

You’ll join our existing data engineering team and help strengthen its infrastructure, tooling, and operating standards as Abaka AI scales. This role is a strong fit for an engineer who enjoys hands-on systems work, takes ownership of delivery, and can turn ambiguous client needs into clear technical plans.

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 work 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 checks that could not be performed, and prevent data that fails agreed specifications from being delivered.

Improve visibility into project and dataset status through tools such as requirement-to-delivery trackers and dataset indexes covering progress, ownership, yield, inventory, quality, cost, sample links, and delivery history.

Help operate the infrastructure behind our data workflows, including object storage, batch processing, compute and GPU resources, orchestration environments, annotation platforms, and internal data services.

Track infrastructure usage and project costs, and identify ways to improve quality, throughput, and efficiency without compromising delivery commitments.

Standardize recurring workflows, automate manual steps, build internal tooling, and document reusable practices based on project retrospectives.

Partner with client-facing teams and foundation model teams to clarify data needs, communicate technical tradeoffs, and raise feasibility or quality risks early.

Minimum 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.

Preferred Qualifications

Experience preparing pretraining, supervised fine-tuning, RLHF, or evaluation datasets for LLMs or multimodal foundation models.

Experience with data for autonomous driving, robotics, embodied AI, or other sensor-based domains.

Experience with large-scale deduplication, data quality scoring, or dataset composition design.

Experience building annotation platforms or internal data tools, or working with external annotation vendors.

Experience modeling and optimizing the cost of GPU- or compute-intensive data pipelines.

Experience establishing engineering standards and improving workflows on a growing team.

The base salary range for this position is $120,000 – $225,000 USD annually. Compensation may vary outside this range based on a candidate’s qualifications, skills, competencies, and experience.

Base salary is one part of the total compensation package at Abaka AI. This role is also eligible for equity and a comprehensive benefits package, including health, dental, and vision coverage, PTO, and a flexible work schedule.

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