---
title: 'Member of Technical Staff, Inference & RL Systems at Magic'
canonical: 'https://feeny.ai/job/member-of-technical-staff-inference-rl-systems-magic-san-francisco-m3ewreg7g147'
type: 'job'
last_seen: '2026-09-09'
---

# Member of Technical Staff, Inference & RL Systems at Magic

- **Company:** Magic
- **Location:** San Francisco, CA
- **Compensation:** $225k–$550k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-02-28
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/magic.dev/427ffdee-d4d1-4a39-a730-4a96435daa67

## Job description

Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pre-training, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.

## ABOUT THE ROLE

As a Research Engineer on the Inference & RL Systems team, you will design and operate the distributed systems that serve our models in production and power large-scale post-training workflows.

This role sits at the boundary between model execution and distributed infrastructure. You will work on systems that determine inference latency, throughput, stability, and the reliability of RL and post-training training loops.

Magic’s long-context models introduce demanding execution constraints: KV-cache scaling, memory pressure under long sequences, batching trade-offs, long-horizon trajectory rollouts, and sustained throughput under real-world workloads. You will own the infrastructure that makes both production inference and large-scale RL iteration fast and reliable.

## WHAT YOU’LL WORK ON

- Design and scale high-performance inference serving systems
- Optimize KV-cache management, batching strategies, and scheduling
- Improve throughput and latency for long-context workloads
- Build and maintain distributed RL and post-training infrastructure
- Improve reliability of rollout, evaluation, and reward pipelines
- Automate fault detection and recovery for serving and RL systems
- Profile and eliminate performance bottlenecks across GPU, networking, and storage layers
- Collaborate with Kernels and Research to align execution systems with model architecture

## WHAT WE’RE LOOKING FOR

- Strong software engineering and distributed systems fundamentals
- Experience building or operating large-scale inference or training systems
- Deep understanding of GPU execution constraints and memory trade-offs
- Experience debugging performance issues in production ML systems
- Ability to reason about system-level trade-offs between latency, throughput, and cost
- Track record of owning critical production infrastructure

## OUR CULTURE

- Integrity. Words and actions should be aligned
- Hands-on. At Magic, everyone is building
- Teamwork. We move as one team, not N individuals
- Focus. Safely deploy AGI. Everything else is noise
- Quality. Magic should feel like magic

Magic strives to be the place where high-potential individuals can do their best work. We value quick learning and grit just as much as skill and experience.

COMPENSATION, BENEFITS, AND PERKS (US)

- Annual salary range: $275K - $550K
- Equity is a significant part of total compensation, in addition to salary
- 401(k) plan with 6% salary matching
- Generous health, dental and vision insurance for you and your dependents
- Unlimited paid time off
- Visa sponsorship and relocation stipend to bring you to SF, if possible
- A small, fast-paced, highly focused team

## About Magic

## Company Overview
- **One-liner**: Magic is building frontier-scale code models to automate software engineering and research, with the goal of achieving safe AGI.
- **Entity Type**: Private (Series B)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2022
- **Founders**: Eric Steinberger, Sebastian De Ro

## Core Business
- **Primary industry/industries**: Artificial Intelligence, Software Development
- **Target customers**: B2B, Enterprise (via code generation models and APIs)
- **Mission or purpose statement**: To safely deploy AGI by automating AI research and code generation, aligning models more reliably than humans can alone.

## Products & Services
- **LTM (Long Term Memory) Models**: A family of frontier-scale LLMs with ultra-long context windows. LTM-1 had a 5,000,000 token context window, and LTM-2-Mini has a 100,000,000 token context window. These models are designed to understand and generate code across entire codebases.
- **AI Software Engineer**: An internal and product-facing system aimed at autonomously performing software engineering tasks, from code generation to complex research.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed.
- **Key Metric**: Total Funding of **$515 million** (across Seed, Series A, and Series B rounds).
- **Notable Investors/Partners**: Nat Friedman, Daniel Gross, CapitalG (Alphabet’s independent growth fund), Elad Gil, Sequoia Capital, Jane Street, Eric Schmidt.
- **Growth Signals**:
    - Rapid headcount growth: 79 employees in 2025 (up ~100% YoY from ~55 in 2024).
    - Partnership with Google Cloud (announced in conjunction with 100M token context windows and new funding).
    - Operates in 12 countries (including Austria, Switzerland).

## Competitive Advantages
- **Ultra-Long Context Windows**: Their proprietary LTM architecture (100M token context) is a significant moat, enabling the model to process entire code repositories in a single pass.
- **Infrastructure Scale**: Possesses "thousands of GB200s" (NVIDIA GPUs) for frontier-scale pre-training, a massive capital advantage.
- **Small, High-Caliber Team**: A deliberate strategy of maintaining a small team of engineers and researchers to focus on a "short list of fundamental research problems."
- **Direct Path to AGI Thesis**: Uniquely focused on using code generation as both the product and the core research path to AGI, differentiating from broader AI labs.

## Strategic Focus
- **Automate AI Research**: Using code models to autonomously design, develop, and evaluate new alignment techniques.
- **Safety & Alignment**: Actively publishing an "AGI Readiness Policy" and engaging in safety research, viewing alignment as a core technical problem to be solved by AI.
- **Post-Training & Applied Team**: Recently announced an Applied Team focused on post-training to explore practical applications of their unreleased LTM2 models.
- **Security Standards**: Advocating for and implementing cyber, physical, and information security standards comparable to the defense and nuclear industry.

## Why Work Here
- **Mission-Driven**: A small team with a shared belief in the positive potential of responsibly deployed AGI. The work is directly tied to solving fundamental research problems.
- **Culture & Values**: Emphasizes integrity, hands-on work ("everyone is building"), teamwork, focus, and quality.
- **Compensation & Benefits**:
    - Competitive salary with significant equity compensation.
    - Unlimited paid time off.
    - 401K with 6% salary matching.
    - Health, dental, and vision insurance for employees and dependents.
    - Access to mental health, financial wellbeing, and telehealth programs.
    - Office catering (chef-prepared lunch and dinner).
- **Work Environment**: In-person team meetings and office work are emphasized. The hiring process includes a take-home assessment and an in-person team meeting.
- **Hiring Process**: Designed to be fair and focused on potential, not just resume credentials. Includes an application review, interview with a senior team member, take-home assessment (~5-8 hours), in-person team meeting, and reference check.

## Sources
1. [magic.dev](https://magic.dev/)
2. [magic.dev/careers](https://magic.dev/careers)
3. [magic.dev/safety](https://magic.dev/safety)
4. [linkedin.com/company/magicailabs](https://www.linkedin.com/company/magicailabs)
5. [theorg.com/org/magic-dev](https://theorg.com/org/magic-dev)

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