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Salary
≈ $14k – $32k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Junior · 2+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Sep 4, 2026.

Overview
Company
Impact
Profile match
ZenteiQ is a deep technology company headquartered in Bengaluru, India, and founded in 2022 out of research at the Indian Institute of Science. The company works on scientific machine learning, combining numerical simulation with neural networks for engineering and industrial modelling problems, and builds training programmes around those methods. It sells to industrial and research customers in India while running education initiatives that push scientific computing skills into the wider engineering workforce.

About ZenteiQ

ZenteiQ is a deep-tech company born out of IISc Bangalore, building Scientific Intelligence Infrastructure: physics-native AI for engineering, manufacturing, energy, mobility and national systems. Rather than wrapping general-purpose language models, we train foundation models (BrahmAI) from scratch to reason over thermal, electromagnetic, structural and materials domains, and put them to work through industrial platforms (KogneX) and a talent OS for engineers and researchers (AhamX). We're backed by the IndiaAI Mission (MeitY) and work closely with IISc, ARTPARK and a national AI Hub Network - building the sovereign AI infrastructure that India's engineering and industrial systems will run on.

About the Role

At ZenteiQ we're building the data foundation that powers next-generation Large Language Models. As a Data Engineer on the corpus team, you'll build scalable pipelines to source, process, curate, filter and prepare large-scale corpora for training and evaluating foundation models.

You'll work closely with AI Researchers, ML Engineers and Platform Engineers to solve challenging data problems across web-scale datasets, unstructured data, multilingual corpora and benchmark datasets.

What You'll Do

  • Source and ingest large-scale datasets from Common Crawl, public web sources, open-source repositories and other data sources.
  • Build scalable data processing pipelines for large unstructured corpora.
  • Develop and optimise distributed data pipelines using tools such as Apache Spark, Ray and Dask.
  • Extract and process content from web pages and documents using Trafilatura and related web/content extraction libraries.
  • Design data cleaning, filtering, quality assessment and curation pipelines for LLM pretraining corpora.
  • Develop heuristic-based filters to remove low-quality, boilerplate, spam, duplicated or otherwise unsuitable content.
  • Implement document-level and corpus-level deduplication strategies.
  • Identify and mitigate benchmark contamination and evaluation-data leakage in training corpora.
  • Prepare and curate benchmark and evaluation datasets.
  • Work with large-scale datasets stored and processed through GCS/GCP.
  • Monitor data quality, pipeline performance, storage and processing efficiency.
  • Collaborate with AI Researchers and ML Engineers to continuously improve corpus quality and training-data pipelines.

What We're Looking For

Must Have

  • 2-5+ years of experience in Data Engineering, AI Data Engineering, ML Data Engineering or a related field.
  • Strong Python programming skills.
  • Experience working with large-scale unstructured datasets.
  • Experience building data processing / ETL pipelines.
  • Strong understanding of data cleaning, filtering, validation and deduplication.
  • Hands-on experience with exact and near-duplicate detection and deduplication.
  • Familiarity with at least one distributed processing framework: Apache Spark, Ray or Dask.
  • Understanding of web-scale data ingestion, such as Common Crawl or other large public datasets.
  • Experience with HTML parsing and web/document extraction libraries such as Trafilatura, BeautifulSoup, lxml or Resiliparse.
  • Familiarity with GCS/GCP and cloud-based data processing.
  • Experience working with large-scale formats such as JSONL, Parquet or Arrow.
  • Strong problem-solving and ownership mindset.

Good to Have

  • Experience with LLMs, Foundation Models or Generative AI.
  • Experience building or maintaining LLM pretraining corpora.
  • Familiarity with Hugging Face Datasets, DataTrove or NVIDIA NeMo Curator.
  • Experience with WARC/WET/WAT and large-scale Common Crawl processing.
  • Knowledge of MinHash, fuzzy or semantic deduplication.
  • Research-level understanding of synthetic data generation techniques for model training and evaluation.
  • Familiarity with Rust for high-performance data processing.
  • Experience with multilingual or domain-specific corpora.

Why ZenteiQ

  • Build real physics-native foundation models from scratch, not another LLM wrapper - deep, defensible technical work.
  • Be part of a nationally recognised mission: one of 8 startups selected under the government's IndiaAI Mission to build a sovereign foundation model.
  • Work alongside IISc-trained scientists and researchers, in a company founded by an IISc professor.
  • See your work land in real industry pilots across automotive, mobility, defence and industrial R&D, with measurable impact.
  • Join a lean, high-calibre team at an early, high-ownership stage.
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