Generative AI Engineer (PhD / PhD Candidate)
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Sign up to see compensation estimate【Company Description】
Oncogen.AI Inc, established in 2025, is an AI startup dedicated to revolutionizing cancer care and research workflow through agentic AI. Founded by a team of senior physicians, oncology specialists, pathologists, and AI scientists, we aim to redefine precision oncology and scholar research.
【Role Description】
We are seeking a highly skilled
Generative AI Engineer
with a completed PhD or who is currently a
doctoral (PhD) student
to join our team.
This role offers flexibility and can be structured as either internship or part-time.
The responsibility includes and not limited to:
- Design, develop, and optimize
generative AI models
tailored for
scholarly and research-oriented use cases
.
- Preprocess, analyze, and curate
domain-specific datasets
to support high-quality model performance.
- Integrate generative AI models into our
agentic AI platform
, enabling intelligent, autonomous workflows.
- Collaborate closely with
data scientists, engineers, and cross-functional teams
to drive innovation across multiple AI-powered solutions.
- Contribute to experimentation, evaluation, and iteration of models to ensure robustness, scalability, and academic relevance.
【Qualifications】
- PhD in Computer Science, AI, Machine Learning, or a related field, or currently enrolled as a PhD student.
- Strong background in generative models (e.g., LLMs, diffusion models, multimodal models).
- Experience with data preprocessing, dataset curation, and model evaluation.
- Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX, TensorFlow).
- Ability to work independently in a remote, research-driven environment.
- Strong communication skills and interest in scholar-focused AI applications.
【Nice to Have】
- Publications in top-tier conferences or journals.
- Experience with agentic AI systems, tool use, or multi-agent architectures.
- Familiarity with academic or research workflows and knowledge systems.
This role is ideal for researchers who want to translate cutting-edge AI research into real-world scholarly tools while maintaining academic flexibility.