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Open nowPosted 353 days ago

Founding Engineer - ML Research

Deccan AI13 open roles

Pay
$160,000 – $320,000 a yearly
Where
Mountain View, CA
Work mode
On site
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Your applicationOpen nowFounding Engineer - ML ResearchDeccan AI · Mountain View, CA

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 353 days ago

The posting

About Us

Deccan AI is AI training, evaluations and Enterprise AI solutions startup headquartered in the Bay Area. We are founded by IIT Bombay, IIM Ahmedabad and ex-Google alumni, and work with some of the top AI frontier labs in the world. Backed by Prosus Ventures and A91 we are expanding the team in US, San Mateo.

About the Role

We’re looking for a Founding ML Research Engineer to help build and scale our AI research backbone from the ground up. This role sits at the intersection of applied machine learning and systems engineering — ideal for someone who thrives in unstructured environments, loves building fast, and obsesses over model performance and data quality.

You’ll work directly with the founding team to prototype, experiment, and ship research-driven features that push the boundaries of what our AI systems can do.

What You’ll Do

  • Design, train, and evaluate ML models (LLMs, diffusion models, or domain-specific architectures).
  • Develop scalable experimentation pipelines for data, model, and evaluation workflows.
  • Work closely with the data and infra teams to optimize training throughput and quality.
  • Contribute to open research, internal benchmarks, and emerging techniques in multimodal or generative AI.
  • Rapidly prototype and productionize research insights into usable tools and models.
  • Define foundational technical culture — set standards for research rigor, documentation, and reproducibility.

What You’ll Bring

  • 3+ years of experience in ML research, applied ML, or ML systems engineering.
  • Deep familiarity with PyTorch / JAX / TensorFlow, and model architectures (Transformers, Diffusion, or RLHF).
  • Strong foundations in data processing, distributed training, and evaluation metrics.
  • Curiosity for emerging ML paradigms (multimodality, self-learning, synthetic data, agentic systems, etc.).
  • Ability to move from research papers → working prototypes → production-ready code.
  • Passion for building from zero to one in a high-velocity startup environment.
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