Overview
News
Technologies
Salaries
Products
People
Growth
Offices
Jobs
Financials

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  • United Kingdom · Licensed UK visa sponsor (Skilled Worker, Senior or Specialist Worker)

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Overview

Apollo Research is an artificial intelligence safety organisation focused on deceptive model behaviour. Its evaluations test whether frontier models scheme or hide their intentions. The laboratory publishes interpretability research and advises policymakers on model risk.

News

News the Guardian 1 month ago
'If you build something vastly smarter than you, it better be on your side': can we stop AI from deceiving us?
The long read: We are used to the idea that our fellow humans might intentionally mislead or manipulate us, but the idea that machines can now do the same is deeply unsettling. Researchers are racing to find solutions before it's too late
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Report
News theatlantic 2 months ago
It May Be Time to Panic About AI
Bots are starting to conspire with one another. Can they be reeled back in?
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News apolloresearch.ai 1 year ago
Stress Testing Deliberative Alignment for Anti-Scheming Training
We partnered with OpenAI to assess frontier language models for early signs of scheming - covertly pursuing misaligned goals - in controlled stress-tests (non-typical environments), and studied a training method that can significantly reduce (but not elim
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News apolloresearch.ai 1 year ago
More Capable Models Are Better At In-Context Scheming
We evaluate models for in-context scheming using the suite of evals presented in our in-context scheming paper (released December 2024) with the most capable new models.
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News apolloresearch.ai 2 years ago
Towards Safety Cases For AI Scheming
We sketch how developers of frontier AI systems could construct a structured rationale - a 'safety case' - that an AI system is unlikely to cause catastrophic outcomes through scheming. Scheming is a potential threat model where AI systems could pursue mi
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News apolloresearch.ai 2 years ago
Frontier Models are Capable of In-Context Scheming
We evaluated six frontier models for in-context scheming capabilities. We found that multiple frontier models are capable of in-context scheming when strongly nudged to pursue a goal.
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News apolloresearch.ai 2 years ago
The Evals Gap
The quality and quantity of evals required to make rigorous safety statements could outpace available evals. We explain
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News apolloresearch.ai 3 years ago
Science - Apollo Research
We conduct fundamental research into the science of scheming and its potential mitigations, and run pre-deployment evaluations of frontier AI systems.
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Tech stack

Technologies named in job postings at Apollo Research over the last 12 months, by layer, with the share of postings for each.
Technologies in use
27
Postings analysed
17
Stack modernity
77/100AI adopter
Languages
Python53%
TypeScript18%
SQL6%
Cloud & platforms
Anthropic35%
OpenAI29%
SIEM18%
AWS12%
AI & ML
LLM47%
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Full Apollo Research tech stack 27 technologies · by team · pay by technology · changes · similar stacks

Salary insights

Compensation ranges based on this company's open jobs
Median
$280k
Typical range
$131k – $396k
Open positions
13
11 Jobs
Junior 36.4%
Middle 18.2%
Senior 27.3%
Staff 18.2%
Level Jobs Median Typical range
Junior 4 $216k $131k – $385k
Middle 2 $290k $290k
Senior 3 $280k $280k – $385k
Staff 2 $348k $300k – $396k
Highest paying role Engineering Manager (Product) $396k
Normalized to USD per year
All salaries at Apollo Research By role, level and city, against the market · 13 jobs with pay

Growth

Hiring Momentum
37/100
Slowing
Open positions
14
0 opened / 2 closed in 30 days
Median time-to-fill
102 days
faster than 12% of the market
ATS activity
Every ~5 hours
Today
Hiring Dynamics 0%
Hiring Focus
The percentage next to each role is its share of the company's job openings over the last 90 days; the arrow shows the shift versus the previous period.
Security
29% ▼
AI/ML
14% ▼
Leadership
14% ▲
Sales
14% ▲
Backend
7% ▼
Finance
7% ▲
Activity Timeline
Added Zapier to stack Sep 2026
Added QuickBooks to stack Sep 2026
Added n8n to stack Sep 2026
Added Model Context Protocol to stack Sep 2026
Added Clean Architecture to stack Sep 2026
Added SIEM to stack Sep 2026
Added DLP to stack Sep 2026
New location: Washington Sep 2026
Added Scrum to stack Sep 2026

Offices

Where the company hires and what each office is for
Cities
2
Hiring now
2
Countries
2
Busiest office
London
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Headquarters Builds here Office with open vacancies Tap a pin for the office
London United Kingdom
HQ
13 open roles 13 posted in 90 days 62% engineering 50-200 est. staff $284k Avg Salary
hires for Security, AI/ML, Data Science
seen since Feb 2026 job postings
Washington United States
1 open roles 1 posted in 90 days 1-10 est. staff $300k Avg Salary
hires for Public Policy
seen since Sep 2026 job postings

Jobs

$204k – $385k per year • In office • Full-Time • 5+ years exp • London
AI Agents
LLM
OpenAI
Anthropic
Red Teaming
Cybersecurity
MITRE ATT&CK
Threat Modeling
Apply
$182k – $238k per year • In office • Full-Time • 2+ years exp • London
Python
LLM
Anthropic
Red Teaming
Apply
$204k – $385k per year • In office • Full-Time • 2+ years exp • London
Python
SQL
Fine-tuning
LLM
Apply
$214k – $280k per year • In office • Full-Time • 5+ years exp • London
AI Agents
LLM
OpenAI
Anthropic
Cybersecurity
Zero Trust
Threat Modeling
Apply
$214k – $280k per year • In office • Full-Time • 5+ years exp • London
AI Agents
LLM
OpenAI
Anthropic
Red Teaming
DevOps
CI/CD
Apply
$222k – $290k per year • In office • Full-Time • 3+ years exp • London
Python
TypeScript
Cursor
Claude Code
OpenAI Codex
DevOps
CI/CD
Cybersecurity
SIEM
DLP
Apply
$283k – $396k per year • In office • Full-Time • 4+ years exp • London
Python
TypeScript
Cursor
Claude Code
OpenAI Codex
Management
Scrum
Apply
$222k – $290k per year • In office • Full-Time • 4+ years exp • London
Python
TypeScript
Cursor
Claude Code
LLM
OpenAI Codex
Cybersecurity
SIEM
Apply
$134k – $267k per year • In office • Full-Time • London
Python
RLHF
Knowledge Distillation
LLM
OpenAI
Anthropic
SFT
Post-training
Model Distillation
Apply
$134k – $267k per year • In office • Full-Time • London
Python
Reinforcement Learning
AI Agents
Interpretability
Apply