Full-Time

Research Systems Engineer

Applied Compute

Applied Compute

11-50 employees

Custom AI models and enterprise agents

Compensation Overview

$190k - $310k/yr

+ Equity

H1B Sponsorship Available

San Francisco, CA, USA

In Person

Category
AI & Machine Learning (1)
Required Skills
LLM
High Performance Computing (HPC)
Data Structures & Algorithms
PyTorch
Reinforcement Learning

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Requirements
  • Experience training or serving large language models.
  • Experience building reinforcement learning environments and evaluations for language models.
  • Proficiency in PyTorch, JAX, or similar machine learning frameworks, with experience in distributed training.
  • Strong experimental design skills, including the ability to set up experiments that answer research questions.
Responsibilities
  • Post-train frontier-scale language models on enterprise tasks and environments.
  • Explore and develop reinforcement learning techniques by co-designing algorithms and systems.
  • Contribute to Alchemy, the data research program for generating signal-rich training environments from production data.
  • Build high-performance internal tools for probing, debugging, and analyzing training runs.
  • Partner with infrastructure engineers to scale training and inference efficiently.
Desired Qualifications
  • A background in pre-training or post-training research.
  • Previous experience in high-performance computing environments or large-scale clusters.
  • Contributions to open-source machine learning research or infrastructure.
  • Demonstrated technical creativity through published research, open-source contributions, or side projects.

Applied Compute builds custom AI models and in-house AI agents for enterprises, a concept called Specific Intelligence. They train proprietary AI on a company’s own data and fine-tune it for specific workflows, operating on a large GPU cluster with an integrated training stack, agent platform, and development tools. They also embed engineers within customer teams to move from idea to deployed solutions in days, not months. Their goal is to give enterprises a competitive edge by deploying production-ready, specialized AI that deeply integrates into operations and is difficult for others to replicate.

Company Size

11-50

Company Stage

Late Stage VC

Total Funding

$160M

Headquarters

San Francisco, California

Founded

2025

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Simplify Jobs

Simplify's Take

What believers are saying

  • DoorDash, Cognition, and Mercor validate demand for production enterprise agents.
  • May 2026 Context Engine expanded the platform beyond training into persistent memory.
  • June 2025 to April 2026 funding reached $160 million, signaling aggressive investor conviction.

What critics are saying

  • The August 2026 $3 billion fundraising talk invites a brutal reset if investors balk.
  • Open-source model customization commoditizes fast; Anthropic, OpenAI, and incumbents can copy workflows.
  • Customer concentration around DoorDash, Cognition, and Mercor makes one lost logo painful.

What makes Applied Compute unique

  • Former OpenAI researchers Yash Patil, Rhythm Garg, and Linden Li built Specific Intelligence.
  • Applied Compute embeds engineers inside customers, accelerating deployment from months to days.
  • Its Context Engine stores enterprise context for continuous agent improvement and lower inference costs.

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Benefits

Health Insurance

Unlimited Paid Time Off

Parental Leave

Meal Benefits

Relocation Assistance

Company Equity

Growth & Insights and Company News

Headcount

6 month growth

21%

1 year growth

27%

2 year growth

27%
The Information
Aug 10th, 2026
Applied Compute in Talks to Double Valuation to $3 Billion on Open-Source Demand

Applied Compute, a year-old startup that helps companies run and customize open-source models with their own data, is discussing a new funding round that would value it at around $3 billion, more than double its valuation from a round announced four months ago, according to a person with ...

RootData
Apr 9th, 2026
Applied Compute raises $80M at $1.3B valuation to build enterprise AI agents

Applied Compute, a startup building dedicated intelligent systems for enterprises, has raised $80 million in new financing, bringing its post-money valuation to $1.3 billion. Kleiner Perkins led the round, with participation from Elad Gil, Lux Capital, Greenoaks, NEO and Hana Bicapital. Total funding now stands at $160 million. The company develops what it calls "Specific Intelligence" — proprietary intelligent agents trained on real company operations and designed for continuous self-evolution. Applied Compute's technology helps enterprises unlock institutional context, train agents for specific workflows and performance standards, and deploy them to production environments.

Applied Compute
Apr 8th, 2026
The Advantage You Own

Applied Compute Raises $80M to Help Enterprises Advance from Generalized to Specific Intelligence

SiliconANGLE Media
Oct 31st, 2025
Former OpenAI researchers launch Applied Compute with $80M in funding

Former OpenAI researchers launch Applied Compute with $80M in funding - SiliconANGLE