Member of Technical Staff, Reinforcement Learning

The RoleWe seek experienced scientists and engineers with deep expertise in post-training large language models through reinforcement learning. You will design and implement RL training pipelines for our diffusion LLMs, develop reward modeling strategies, and build the algorithms that align model behavior with human intent at scale.Key ResponsibilitiesDesign, develop, and optimize RL training pipelines (PPO, DPO, RLHF, and novel approaches) for diffusion-based LLMs.Build and iterate on reward models, reward shaping strategies, and evaluation of reward quality.Implement innovative approaches for fine-tuning and scaling generative AI models.Work on data preprocessing pipelines, model evaluation, and alignment to enterprise use cases.Research and implement techniques for controlled text generation and constraint satisfaction.Improve training stability, efficiency, and reproducibility of RL workloads.QualificationsBS/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), preferably in a research lab or engineering role.Excellent familiarity with transformers and core LLM concepts (autoregressive pretraining, instruction tuning, in-context learning, KV caching).Hands-on experience with reinforcement learning from human feedback (RLHF), PPO, DPO, or related post-training methods.Familiarity with training and inference in diffusion models.Experience training deep learning models at scale in distributed computing environments.Preferred SkillsExtensive experience training transformer-based language models from scratch.Experience designing and implementing reward models or preference learning systems.Knowledge of advanced training techniques (mixed precision, gradient accumulation, etc.).Background in optimization theory and neural network architecture design.Experience with LLM serving frameworks like vLLM, SGLang, or TensorRT.Why Join InceptionWork with World-Class Talent: Collaborate with the inventors of diffusion models and leading AI researchersShape Foundational Technology: Your decisions will influence how the next generation of AI products are built and usedImmediate Impact: Join at the ground floor where your contributions directly shape product direction and company trajectoryPerks & BenefitsCompetitive salary and equity in a rapidly growing startupFlexible vacation and paid time off (PTO)Health, dental, and vision insuranceCatered meals (breakfast, lunch, & dinner)Commuter subsidiesA collaborative and inclusive cultureAbout UsInception creates the world's fastest, most efficient AI models. Today's autoregressive LLMs generate tokens sequentially, which makes them painfully slow and expensive. Inception's diffusion-based LLMs (dLLMs) generate answers in parallel. They are 5x faster and more efficient, while delivering best-in-class quality.Inception was co-founded by Stanford professor Stefano Ermon, who co-invented such breakthrough AI technologies as diffusion models, flash attention, and DPO, UCLA professor Aditya Grover, who co-invented node2vec, decision transformers, and d1 reasoning, and Cornell professor and Afresh co-founder Volodymyr Kuleshov, who co-invented MDLM and Block Diffusion.We pioneered the application of diffusion to language, with world's first (and only) commercially available dLLM, Mercury. We are currently deploying our large-scale diffusion LLMs at Fortune 500 companies. Diffusion is the technology behind today's image and video AI, and we're making it the standard for LLMs as well.Our team includes engineers from Google DeepMind, Meta AI, Microsoft AI, and OpenAI. Based in Palo Alto, CA, we are backed by A-list venture capitalists, including Menlo Ventures, Mayfield, M12 (Microsoft's venture fund), Snowflake Ventures, Databricks, and Innovation Endeavors, and by tech luminaries such as Andrew Ng, Andrej Karpathy, and Eric Schmidt.If you are talented, innovative, and ambitious, come help us invent the future of AI.We are an equal opportunity employer and encourage candidates of all backgrounds to apply.

Created: 2026-04-02