Research Scientist / Engineer
Inception Labs
San Francisco, CA, USA
Posted on Aug 7, 2025
Research Scientist / Engineer
Bay Area
Engineering
In office
Full-time
About Us
Inception is a generative AI startup. Leveraging breakthrough AI research, we are training next-generation large language models (LLM) powered by diffusion. Unlike existing auto-regressive models, which only output one token at a time, diffusion LLMs can output many tokens in parallel. This means that they are several times faster and can leverage their additional test-time compute to improve quality. They also enable fine-grained control over their outputs to adhere to specific schema and semantic constraints, and they provide a unified paradigm for combining language with other data modalities, including audio, images, and videos.
Our team is led by Stefano Ermon (co-inventor of diffusion models, flash attention, and DPO; faculty at Stanford), Aditya Grover (co-inventor of node2vec and decision transformers; faculty at UCLA), and Volodymyr Kuleshov (prev. co-founder and CTO at Afresh Technologies; faculty at Cornell), and includes engineers from Google Deepmind, Meta AI, Microsoft AI, and OpenAI. We are currently deploying large-scale diffusion LLMs at Fortune 500 companies.
Role Overview
We are looking for Research Scientists / Engineers with deep expertise in training and optimizing large language models. In this role, you will work on advancing our diffusion-based LLM architecture, developing novel training techniques, and pushing the boundaries of what's possible with parallel token generation.
Key Responsibilities
- Design, develop, and optimize LLM architectures and models.
- Implement innovative approaches for training, fine-tuning, and scaling generative AI models.
- Work on data preprocessing pipelines, model evaluation, and alignment to enterprise use cases.
- Design and implement novel model architectures for diffusion-based language models
- Develop and optimize training objectives and loss functions for LLMs
- Research and implement techniques for controlled text generation and constraint satisfaction
- Develop methods for multi-modal integration within the diffusion framework
- Work on improving model efficiency, reducing training time, and optimizing inference
Qualifications
- BS/MS/PhD in Computer Science or a related field (or equivalent experience).
- At least 2 years of experience working on ML projects in PyTorch (or equivalent DL framework), preferably in a research lab or engineering role.
- Excellent familiarity with transformers and fundamental LLM concepts (e.g., autoregressive pretraining, instruction tuning, in-context learning, and KV caching).
- Familiarity with training and inference in diffusion models.
- Experience with training deep learning models at scale using distributed computing environments.
Preferred Skills
- Extensive experience training transformer-based language models from scratch
- Knowledge of advanced training techniques (mixed precision, gradient accumulation, etc.)
- Experience with multi-modal learning and cross-modal architectures
- Background in optimization theory and neural network architecture design
- Experience with LLMs serving frameworks like vLLM, SGLang, or TensorRT.
Why Join Us
- Impact: Deploy LLMs that transform how millions of users work, create, and solve real-world problems.
- Innovation: Pioneer novel architectures and training techniques for diffusion LLMs.
- Growth: Enjoy a fast-paced, collaborative environment where your contributions will directly shape the future of generative AI.
Perks & Benefits
- Competitive salary and equity in a rapidly growing startup.
- Flexible vacation and paid time off (PTO).
- Health, dental, and vision insurance.
- Professional development opportunities (conferences, courses, etc.).
This is an exciting opportunity to join a startup at the forefront of AI development! If you’re ready to make a tangible impact in the world of generative AI, apply today.
We are an equal opportunity employer and encourage candidates of all backgrounds to apply.
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Req ID: R4