AI Scientist

Are you passionate about
Generative AI
and want to apply it to one of the most impactful domains —
cybersecurity
?

Join our cutting-edge startup in the
San Francisco Bay Area
, where we are developing AI systems that transform how organizations understand, detect, and respond to cyber threats.

As an
Applied AI Scientist
, you'll bridge AI research and real-world cybersecurity use cases — designing, implementing, and optimizing models that extract, reason, and act on complex security data.

You'll work closely with
cybersecurity experts, AI infrastructure engineers, and stakeholders
to build end-to-end GenAI solutions: from concept to deployment.

This role blends
deep applied research
with
practical engineering
, ideal for someone eager to push the limits of Generative AI for meaningful impact.

Why Join Us:

• $25M Seed Funding:
  Strong capital foundation to innovate and scale fast.
• Early Success:
  Trusted by
  Fortune 500 companies
  , validating real-world demand.
• Experienced Leadership:
  Founders with 25+ years in cybersecurity — previous ventures valued at $3B+.
• Elite AI Leadership:
  Heads of AI, Engineering, and Product from world-class tech companies.
• Advanced AI Stack:
  LLMs, embeddings, RAG systems, LangGraph orchestration, and multimodal AI.
• Competitive Compensation:
  Excellent salary, meaningful equity, and room for technical leadership growth.
• Cybersecurity Knowledge Preferred but Not Required:
  We'll teach you the domain — you bring the AI innovation.

Key Responsibilities:

Core Applied AI Research

• Collaborate with
  cybersecurity researchers and stakeholders
  to scope AI-driven solutions to security problems (e.g., vulnerability management, code analysis, threat detection).
• Conduct
  applied research
  using the latest
  LLMs and embedding models
  (Claude, Google GenAI, Unsloth, vLLM).
• Prototype, fine-tune, and evaluate
  GenAI and RAG/CAG architectures
  for classification, summarization, reasoning, and context synthesis.
• Perform
  embedding-level optimization
  for text, code, and image data using Unsloth, Hugging Face, Voyage, or similar frameworks.

System Development & Integration

• Develop and test
  end-to-end AI pipelines
  integrating Milvus or Pinecone for semantic retrieval.
• Build
  agentic AI systems
  using LangGraph or similar frameworks to enable autonomous reasoning and task chaining.
• Collaborate with
  MLOps engineers
  to deploy and monitor AI models in production securely and efficiently.
• Contribute to
  synthetic data generation pipelines
  for fine-tuning and evaluation.

Evaluation & Optimization

• Implement
  evaluation frameworks
  using DeepEval and GenAI tools (Claude / Google GenAI) for factuality, reliability, and robustness.
• Optimize model performance across latency, accuracy, and cost using vLLM, quantization, or caching strategies.
• Maintain
  reproducible experiment tracking
  with MLflow, Weights & Biases, or internal tools.

Innovation & Leadership

• Stay ahead of GenAI trends — multi-modal reasoning, agentic orchestration, embedding adaptation.
• Explore
  hybrid LLM deployment strategies
  (local Unsloth/vLLM + cloud APIs like Claude, Google GenAI).
• Document best practices, share learnings, and mentor junior scientists on applied GenAI workflows.

Qualifications:

Required

• 4+ years in
  Applied AI / Machine Learning Research / Data Science
  .
• Strong understanding of
  LLMs, embeddings, RAG systems, and multimodal learning
  .
• Proficiency in
  Python
  and frameworks like
  PyTorch, Transformers, Hugging Face, or LangChain
  .
• Experience in
  prompt engineering
  ,
  model evaluation
  , and
  retrieval-based reasoning
  .
• Hands-on experience with
  vector databases (Milvus / Pinecone)
  and
  orchestration frameworks (LangGraph / LangChain)
  .
• Strong communication skills and ability to collaborate across research and engineering functions.

Preferred

• Experience with
  fine-tuning LLMs or embeddings
  using Unsloth or similar frameworks.
• Familiarity with
  Claude / Google GenAI
  APIs for cloud-based inference and evaluation.
• Exposure to
  cybersecurity or enterprise data
  (CVEs, pluginText, network or asset logs).
• Prior work on
  synthetic data generation
  and evaluation frameworks (DeepEval).
• Experience in a
  fast-paced startup or applied research environment
  .

Our Culture & Team

•
Collaborative and Mission-Driven:
Every project directly advances global cybersecurity.

•
World-Class Mentorship:
Work with senior experts from top AI and security companies.

•
Growth-Oriented:
Opportunities to lead GenAI initiatives and own major research tracks.

•
Inclusive and Innovative:
We value diverse perspectives and open experimentation.

Perks & Benefits

• Comprehensive medical, dental, and vision coverage.
• Wellness and professional development stipends.
• Equity options — your impact equals ownership.
• Access to
  state-of-the-art GPUs, APIs, and GenAI frameworks
  .