Generative AI Applied Scientist

Palo Alto, California

Generative AI Applied Scientist

Key Responsibilities

• Proactively 

  identify high-value problem areas

   where generative AI can deliver measurable impact.
• Translate ambiguous or emerging needs into well-defined ML tasks and deliver 

  production-grade solutions

  .
• Design and develop custom 

  generative models

   (e.g., Transformers, LLMs, Diffusion Models, VAEs).
• Own the end-to-end lifecycle of model development — from 

  prototyping to cloud/API deployment

  .
• Good communication skills that involve being able to talk to customers, identify problem statements, and communicate impactful results.
• Collaborate across research, product, and engineering teams to embed generative AI into vertical solutions.

Required Qualifications

• PhD or Masters with Thesis and 5+ years in Computer Science, Machine Learning, AI, or a closely related field.
• Strong research track record with publications in top-tier conferences (e.g., 

  IEEE TPAMI, CVPR, ICML, ICLR, NeurIPS, ACL

  ).
• Expertise in 

  deep generative models

  , 

  representation learning

  , and 

  probabilistic methods

  .
• Experience in building models that solve real-world problems and can be 

  scaled into production systems

  .
• Fluency in Python and modern ML stacks (e.g., PyTorch, JAX, Hugging Face).
• Willingness to work 

  onsite in Mysore

   and collaborate across time zones with the US-based HQ.
• Involves travel about 25-50% to client locations.

Preferred Qualifications

• Experience in 

  LLM fine-tuning

  , 

  multimodal systems

  , or 

  diffusion-based generation

  .
• Familiarity with 

  retrieval-augmented generation

  , 

  causal inference

  , or 

  workflow agents

  .
• Experience deploying models via Docker, ONNX, or cloud-native APIs (AWS/GCP).
• A track record of discovering novel application areas and developing first-of-their-kind solutions.

What We Offer

• A fast-paced startup environment backed by cutting-edge research and global customers.

• ESOPs/Equity

   and competitive compensation.
• Access to high-performance compute (A100s, H100s), internal datasets, and modern MLOps tooling.
• Clear runway for 

  career growth

  , 

  technical leadership

  , and 

  IP ownership

  .
• A collaborative and ambitious team working on some of the most important problems in applied AI — from 

  Mysore

   and 

  Palo Alto

  .