Kaiko’s Multimodal Large Language Model (MLLM) is trained on domain-specific, high-complexity medical data. To reach clinical-grade performance, we’ll need to ramp up our data efforts to manage massive scale, ensure consistent quality, and tightly control data relevance and integrity.

As a Senior Research Data Engineer, you will design and implement our data‑sourcing, synthetic‑generation, and curation pipelines. High‑quality datasets are the fuel for frontier‑scale language models, and you will play a pivotal role in producing them.

You will build high‑throughput data pipelines that:

• Ingest multi‑modal data at petabyte scale.
• Generate large volumes of synthetic data.
• Filter & rate content by topic, quality, and policy compliance.

You will work closely with ML researchers and help steer the development of our state‑of‑the‑art foundation models. 

Profile

• Excellent programming skills in Python and deep experience with distributed frameworks such as Ray or Spark.
• Proven track record designing & operating large‑scale data pipelines and running data‑quality experiments.
• Experience building or integrating synthetic‑data pipelines for LLMs.
• Deep familiarity with lakehouse paradigms (Delta, Iceberg) and columnar formats (Parquet, ORC).
• Experience with core data‑processing primitives (hashing, deduplication, chunking etc.) and associated scalability/performance trade‑offs.
• Strong communication skills and the ability to present experimental results and technical concepts clearly and concisely.

Nice To Have:

• Hands‑on production experience orchestrating complex DAGs in Dagster (preferred) or similar workflow engines.
• Expertise in data‑quality & validation frameworks and monitoring/observability tooling.