Machine Learning Engineer

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 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 ResponsibilitiesDesign, develop, and optimize LLM architectures and models.Partner with customers to understand their use cases and translate business requirements into technical solutionsImplement innovative approaches for training, fine-tuning, and scaling generative AI models.Work on data preprocessing pipelines, model evaluation, and alignment to enterprise use cases.Contribute to the deployment and maintenance of models in production environments.Collaborate with product teams to design and implement customer-facing ML features
QualificationsBS/MS/PhD 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 LLM, including fine-tuning.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 SkillsExpertise 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 UsImpact: 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 & BenefitsCompetitive 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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