AI Scientist / GenAI Engineer

Are you passionate about

Generative AI

and want to apply it to one of the most impactful domains —

cybersecurity ?

Apply now, read the job details by scrolling down Double check you have the necessary skills before sending an application.

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. xsgimln Access to

state-of-the-art GPUs, APIs, and GenAI frameworks .