Data Engineering Lead (AI)

Location: San Jose, United States

Salary: $170,000–$450,000 annually

Company

hirify.global is an artificial intelligence company developing multimodal models and next-generation AI hardware for natural interaction through speech, text, vision, and persistent memory.

What you will do

• Design and build scalable data pipelines for text, audio, vision, and structured feedback signals.
• Own ingestion, transformation, deduplication, quality filtering, versioning, and delivery to model training and evaluation systems.
• Translate data requirements from model researchers and data collection leads into reliable, auditable pipelines.
• Build tooling for inspecting, evaluating, and improving data quality and feed insights back into collection and curation.
• Define quality standards and instrument pipelines for correctness, freshness, coverage, reproducibility, and scale.
• Identify gaps and improve data throughput, quality, and reliability.

Requirements

• Strong data engineering fundamentals and experience operating large-scale batch and streaming pipelines.
• Experience building data systems for machine learning and model training.
• Experience with modern data tools such as Spark, Beam, or Flink.
• Systems thinking covering schema evolution, backfills, failure modes, and production reliability.
• Strong data quality instincts and communication skills for working with researchers and engineers.
• 5+ years of relevant data engineering experience.

Nice to have

• Experience with data versioning systems such as DVC, Delta Lake, or Iceberg.
• Experience with large language model or multimodal model training infrastructure.
• Familiarity with audio, video, and image processing pipelines.
• Experience with human feedback or preference data pipelines, including RLHF or DPO.
• Experience with data quality evaluation frameworks, annotation tooling, distributed systems, stream processing, or large-scale ETL.

Culture & Benefits

• High-ownership role on a small team.
• Direct collaboration with model researchers, data collection leads, and infrastructure engineers.
• Systems built in the role directly influence model quality and development pace.
• Full-time compensation may include additional compensation components and benefits.