About Us
Inception is a generative AI startup. Leveraging breakthrough AI research, we have developed a platform for 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 seek experienced Machine Learning Engineers passionate about bringing cutting-edge AI to production. In this role, you will bridge the gap between research and real-world applications, working to train and deploy our diffusion large language models while collaborating with a cross-functional team of researchers and engineers. You'll be instrumental in building our core product offerings while ensuring models perform reliably at scale in production environments.
Key Responsibilities
- Design, develop, and optimize LLM architectures and models.
- Partner with customers to understand their use cases and translate business requirements into technical solutions
- Implement innovative approaches for training, fine-tuning, and scaling generative AI models.
- Work on data preprocessing pipelines, model evaluation, and alignment to enterprise usecases.
- Contribute to the deployment and maintenance of models in production environments.
- Collaborate with product teams to design and implement customer-facing ML features
Qualifications
- BS/MS in Computer Science, Machine Learning, or 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, LoRA, and KV caching).
- Experience with training deep learning models at scale using distributed computing environments.
- Familiarity with large-scale systems and high-performance computing, including GPU/TPU utilization.
- Experience with version control (Git) and containerization (Docker).
- Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders
Preferred Skills
- Expertise in data engineering and synthetic data generation for LLMs.
- Knowledge of MLOps and production-level deployment workflows.
- Experience with LLMs serving frameworks like vLLM, SGLang, or TensorRT.
- Experience with cloud platforms (AWS, GCP, Azure)
- Experience with model quantization and optimization techniques
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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