---
title: 'Member of Technical Staff, Reinforcement Learning at Inception'
canonical: 'https://feeny.ai/job/member-of-technical-staff-reinforcement-learning-inception-san-francisco-bay-18er335yy4aa'
type: 'job'
last_seen: '2026-09-13'
---

# Member of Technical Staff, Reinforcement Learning at Inception

- **Company:** Inception
- **Location:** San Francisco Bay Area, CA
- **Compensation:** $200k–$350k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-03-10
- **Last confirmed live:** 2026-09-13
- **Apply:** https://jobs.gem.com/inception/am9icG9zdDpJ1umZangltW2CoSF9uK96

## Job description

## The Role

We 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 Responsibilities

- Design, 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.

## 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), 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 Skills

- Extensive 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.

## About Inception

## Company Overview
- **One-liner**: Inception is an AI research and product company building diffusion-based language models (dLLMs) for production applications, offering 5x faster inference than traditional autoregressive LLMs.
- **Entity Type**: Private (Seed-stage; $57M total funding)
- **Headquarters**: Palo Alto, California, United States
- **Founded**: Not publicly available (earliest funding round dated March 2025)
- **Founders**: Stefano Ermon (CEO), Aditya Grover (Co-Founder & CTO), Volodymyr Kuleshov (Co-Founder)

## Core Business
- **Primary industry**: Artificial intelligence research and infrastructure; large language models (LLMs)
- **Target customers**: B2B developers, enterprises, and AI application builders requiring low-latency, high-volume LLM inference for multi-step agents and real-time products.
- **Mission or purpose**: “We’re building the next generation of LLMs” – enabling intelligent, responsive, and scalable AI applications through diffusion-based architectures.

## Products & Services
- **Mercury (dLLM family)**: The world’s first commercially available family of diffusion large language models. Generates output via a coarse-to-fine iterative refinement process over a small number of steps, achieving 5x greater speed than autoregressive models while maintaining best-in-class quality. Seamlessly integrates into existing LLM workflows.
- **Research technologies**: d1 Reasoning, Discrete Diffusion Guidance, Direct Preference Optimization, Flash Attention, Decision Transformers, and Diffusion Models – foundational contributions that underpin the company’s product.

## Market Standing
- **Valuation / Market Cap**: Not disclosed
- **Key Metric**: Total funding of $57M across two seed rounds (2025)
- **Notable Investors / Partners**: Menlo Ventures (led the $50M seed round), Amazon Web Services (non‑equity assistance), Eric Schmidt (individual investor)
- **Growth Signals**: Headcount grew 187.5% year‑over‑year to 39 employees; LinkedIn followers increased 240% yearly to ~11,800; active job postings across AI systems, engineering, research, product, and marketing; talent sourced from Google DeepMind, Stanford SAIL, Meta, Amazon, and Pixxel.

## Competitive Advantages
- **Diffusion paradigm**: Non‑autoregressive generation that allows parallel refinement and revision during inference, breaking the sequential token‑by‑token bottleneck of conventional LLMs.
- **Speed‑cost curve**: Delivers 5x faster output with a fundamentally different cost structure, critical for real‑time, multi‑step agent workflows.
- **Founding team pedigree**: Co‑founders have pioneered breakthroughs in model architectures (Flash Attention, Direct Preference Optimization) and have deep experience turning research into production systems at scale.

## Strategic Focus
- Scaling Mercury adoption by integrating dLLMs into existing LLM pipelines and targeting high‑volume, latency‑sensitive applications.
- Advancing research in diffusion‑based reasoning, reinforcement learning for language models, and training/serving infrastructure.
- Expanding the team across AI systems (kernels, inference, training infra), product engineering, and research to accelerate product‑market fit.

## Why Work Here
- **Culture**: A tight‑knit team of scientists, engineers, and builders focused on shipping frontier AI research. Emphasis on innovation, speed, and real‑world impact.
- **Work policy**: In‑office for most roles (Palo Alto HQ); a Marketing Intern position is listed as remote. Typical time on‑site is full‑time in the office.
- **Notable perks / engineering culture**: Opportunity to work on cutting‑edge diffusion LLMs from scratch, contribute to foundational research (papers published), and collaborate with alumni from top AI labs. Roles span from kernel engineering to RL infrastructure and product management.
- **Growth**: Rapidly scaling headcount (+187% YoY) with open positions across multiple disciplines, offering strong career progression in a high‑visibility startup.

## Sources
1. [inceptionlabs.ai/about](https://www.inceptionlabs.ai/about)
2. [inceptionlabs.ai/careers](https://www.inceptionlabs.ai/careers)
3. [jobs.gem.com/inception](https://jobs.gem.com/inception)
4. [linkedin.com/company/inception-labs-ai](https://linkedin.com/company/inception-labs-ai)
5. [builtin.com/company/inception](https://builtin.com/company/inception)

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