Member of Technical Staff, Distributed Systems Engineer at Mirendil (San Francisco, CA)

Mirendil· San Francisco, CA·20h ago

Role details

Work type

Onsite

Employment

Full-Time

Job description

Mirendil Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We are building a frontier AI research company and training our own models end-to-end.

The Role

We have a lot of compute, and a lot more work for it. We’re looking for a Systems Engineer who knows how to find bottlenecks and move them somewhere we can live with. Your work determines how much goodput we get out of our compute. Some example areas you might work on:

AI workload and system co-design - rethink the AI application, execution, and hardware choices together to improve performance across the stack
Performance engineering - profile across the stack, find where time and resources go, and fix what matters
Reliable distributed systems - design how work and data move across machines, balancing parallelism, communication, and correctness under load and failure
Heterogeneous compute and workloads - make large-scale mixes of sandboxes, agent harnesses, training, and inference run efficiently together across CPUs and accelerators

We offer a base salary of $300,000–$400,000 and a meaningful equity grant, depending on experience and background, along with competitive benefits.

Why work at Mirendil

Engineering culture: “Small team, singular focus. We hire to compound, not to grow.” The team of ~20 operates with high autonomy and ownership.
Cutting-edge work: Opportunity to work on one of the most ambitious AI projects – automating the very process of AI research.
Team composition: Colleagues from Anthropic, xAI, DeepMind, OpenAI; founders known for foundational contributions (SAM optimizer, Blueshift/Mirerva).
Work environment: Based in San Francisco; likely in-office given the intensity of the work (remote/hybrid policy not publicly detailed).
Notable perks: Direct exposure to frontier model training and infrastructure; chance to shape a company from its earliest stage.

About Mirendil

Company Overview

One-liner: Mirendil is building an AI system that automates AI research and development, with the goal of creating a self-improving loop that accelerates scientific discovery.
Entity Type: Private (Seed stage; raised $200M)
Headquarters: San Francisco, California, USA
Founded: Early 2026
Founders: Behnam Neyshabur and Harsh Mehta

Core Business

Primary industry: Artificial Intelligence / AI Research & Development
Target customers: B2B – initially AI researchers and engineers, but eventually scientists and domain experts (e.g., biology labs, drug discovery teams) who need frontier AI capabilities without building a full AI lab.
Mission: “Democratizing frontier AI R&D to accelerate science and technology.” The company believes self-accelerating AI R&D is the most direct path to solving humanity’s pressing problems.

Products & Services

Mirendil Platform: A proprietary AI R&D system that trains frontier models specialized in AI research tasks (e.g., experimental design, hyperparameter search, model evaluation, code writing, debugging). The platform is designed to autonomously loop over research and engineering problems, controlling its own GPUs and improving over time with minimal human input. Status: Not yet shipped; under development.

Market Standing

Valuation: $1 billion (as of June 2026 seed round)
Key Metric (Funding): $200 million raised in seed round – one of the largest AI seed rounds ever.
Notable Investors/Partners: Andreessen Horowitz (co-lead), Kleiner Perkins (co-lead), NVIDIA (participant).
Growth Signals:
- Raised $200M at $1B valuation just months after founding.
- Founding team of 20+ researchers and engineers recruited from Anthropic, xAI, Google DeepMind, and OpenAI.
- Founders Behnam Neyshabur (co-inventor of SAM optimizer, ex-Google, ex-Anthropic) and Harsh Mehta (ex-Google, ex-Anthropic, initiated automated AI R&D efforts) have deep frontier AI experience.
- Targeted at compressing research cycles from months to days.

Competitive Advantages

Self-Accelerating Loop: The product is not just a model but a system that improves itself – “the loop is the product.” This creates a compounding advantage if it works.
Singular Focus: The entire company is rebuilt from scratch around AI doing AI research, unlike traditional labs that use AI as a tool.
Top-tier Talent Density: With only ~20 people, the team includes some of the most accomplished young researchers from the leading AI labs.
Strategic Investors: Backing from a16z, Kleiner Perkins, and NVIDIA provides credibility, capital, and potential infrastructure access.

Strategic Focus

Immediate goal: Build the system that can autonomously perform AI R&D, making frontier research accessible to non-AI experts.
Long-term vision: Democratize frontier AI R&D so that any lab (drug discovery, chemistry, biology, robotics) can leverage it without becoming a frontier AI lab themselves.
Current priorities: Train models exceptional at AI research; build infrastructure for automated experimentation, evaluation, and iteration; keep the team small and high-compound.

Sources

Job description

The Role

Why work at Mirendil

Application questions