---
title: 'Member of Technical Staff, RL Infra at Inception'
canonical: 'https://feeny.ai/job/member-of-technical-staff-rl-infra-inception-bay-area-4q6ej1rbc1b2'
type: 'job'
last_seen: '2026-09-05'
---

# Member of Technical Staff, RL Infra at Inception

- **Company:** Inception
- **Location:** Bay Area
- **Compensation:** $200k–$350k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-03-10
- **Last confirmed live:** 2026-09-05
- **Apply:** https://jobs.gem.com/inception/am9icG9zdDoRkGsEqdvAYd3vDf7GIez6

## Job description

## The Role

We're looking for engineers and scientists to design, optimize, and maintain the core systems that enable scalable, efficient reinforcement learning for large models. This role sits at the intersection of research and large-scale systems engineering: you'll wear many hats, from optimizing rollout and reward pipelines to enhancing reliability, observability, and orchestration, collaborating closely with researchers to make RL stable, fast, and production-ready.

## Key Responsibilities

- Design, build, and optimize the infrastructure that powers large-scale reinforcement learning and post-training workloads.
- Improve the reliability and scalability of RL training pipelines, distributed RL workloads, and training throughput.
- Develop shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility for RL systems.

## Qualifications

- BS/MS/PhD in Computer Science, Engineering, or a related field (or equivalent experience).
- Understanding of ML frameworks (PyTorch, TensorFlow, Ray, Megatron) from a systems perspective.
- Experience working with reinforcement learning workloads (PPO, DPO, RLHF, or reward modeling).
- Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.

## Preferred Skills

- Experience building and maintaining large-scale language models with tens of billions of parameters or more.
- Experience with ML workflow orchestration tools (Kubeflow, Airflow).
- Background in performance optimization and profiling of ML systems.

## 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)

## Other roles at Inception

- [Developer Relations](https://feeny.ai/job/developer-relations-inception-bay-area-9gb5d35p4ack) — Bay Area
- [Member of Technical Staff, Security Engineering](https://feeny.ai/job/member-of-technical-staff-security-engineering-inception-bay-area-bkgnvmqqqyg1) — Bay Area
- [Member of Technical Staff, Forward Deployed AI Engineer](https://feeny.ai/job/member-of-technical-staff-forward-deployed-ai-engineer-inception-bay-area-n37pn7zzk4sr) — Bay Area
- [Marketing Intern - AI/ML](https://feeny.ai/job/marketing-intern-ai-ml-inception-bay-area-hqv1w6vc88sy) — Bay Area
- [Member of Technical Staff, Software Engineer](https://feeny.ai/job/member-of-technical-staff-software-engineer-inception-bay-area-b1ts9n512wcr) — Bay Area
- [Member of Technical Staff, Backend, LLM Applications](https://feeny.ai/job/member-of-technical-staff-backend-llm-applications-inception-bay-area-55k8x9hxv3x9) — Bay Area
- [Member of Technical Staff, Full Stack, LLM Applications](https://feeny.ai/job/member-of-technical-staff-full-stack-llm-applications-inception-bay-area-km5kydrykfam) — Bay Area
- [Member of Technical Staff, Data Infrastructure](https://feeny.ai/job/member-of-technical-staff-data-infrastructure-inception-bay-area-2p6nfm2vwe2b) — Bay Area
- [Member of Technical Staff, Training Infra](https://feeny.ai/job/member-of-technical-staff-training-infra-inception-bay-area-na2qhxfw1grw) — Bay Area
- [Member of Technical Staff, Kernels](https://feeny.ai/job/member-of-technical-staff-kernels-inception-bay-area-z2gjybc11h41) — Bay Area