Firetiger
firetiger.com
Actively hiring
AI Agents
Observability & APM
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How Firetiger hires
Truth index
B 75/100
Ghost roles
0%
Stale roles
100%
Reposted roles
0%
Firetiger scores B on the Alion truth index (A is best, E worst), measured over 4 open roles re-checked against its own hiring board: 0% look like ghost listings, 100% have gone stale and 0% are the same role posted again. It measures how much of what this employer publishes turns into a real, finite search - not the employer as a workplace. How the index is computed
News
Firetiger joins Cursor Cursor
Firetiger builds agents that work on software once it reaches production. They're joining Cursor to close the loop from writing code to running it.
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Migrating from Claude to DeepSeek without breaking everything
Firetiger runs a fleet of agents that investigate incidents, monitor deployments, and dig through telemetry on behalf of our customers. Every one of those agents is, at its core, a loop around an LLM, which means our single biggest cost of goods sold is i
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Agentically optimizing LLM prompt cache TTLs for fun and profit
A case study on production objective hill climbingFiretiger runs a few hundred large language model (LLM) agents in production, and prompt caching is a critical tool to manage the cost of running such a workload. Properly setting cache time-to-live (TTL),
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Firetiger Change Monitors: does your PR do what it says on the tin?
Today, we're making Change Monitors generally available for all Firetiger customers.Firetiger Change Monitors pair with every pull request to form a per-change safety net: an agent reads your diff, studies the systems it touches and how they behave in pro
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Agent SLOs: Grounding autonomous agents in metrics that matter
Today we're shipping Agent SLOs, a new feature that gives every Firetiger agent a structured way to track the health of the missions it's assigned. Agents now define their own service level objectives (SLOs), evaluate them every session, and use those eva
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Agent Engineering Patterns: Dealing with very large tool results
Agents are models with tools in a loop. Your first job then, when building an agent, is to give them useful tools. But wasting tokens on unimportant information is catastrophic for agent brains, so you need to be careful and protect your agents from tools
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We built an observability database for agents, not humans
Firetiger helps you engineer outcomes: we build AI agents that operate and improve your software. To operate and improve software, you need
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Custom programming languages make agents really, really good
Agents are good at both creating and using new domain-specific languagesYou're probably sleeping on how effective domain-specific languages have become in the last few months. If you're building a product on top of LLMs and agents (and even if you aren't,
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Networking with Agents: How to Put Them in the Right Conversations
Connect Firetiger to your Tailscale Tailnet, allowing Firetiger DBAs and other Agents to securely observe and operate your privately networked infrastructure.
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Free-range agentic parenting: If you love your agents, set them free!
The hardest part of building autonomous agents is not making them smart. It is deciding what to do when they are smarter than what you designed for. As we build agents at Firetiger, we keep running into this problem. We always ask ourselves whether we sho
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Tech stack
Technologies named in job postings at Firetiger over the last 12 months, by layer, with the share of postings for each.
Technologies in use
9
Postings analysed
4
Stack modernity
76/100
Languages
SQL
Frameworks & libraries
FastAI
PyTorch
Cloud & platforms
Cloudflare
Datadog
Anthropic
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Full Firetiger tech stack
9 technologies · by team · pay by technology · changes · similar stacks
Salary insights
Compensation ranges based on this company's open jobs
Median
$120k
Typical range
$120k – $170k
Open positions
3
3
Jobs
Intern
33.3%
Junior
33.3%
Middle
33.3%
| Level | Jobs | Median | Typical range |
|---|---|---|---|
| Intern | 1 | $120k | $120k |
| Junior | 1 | $120k | $120k |
| Middle | 1 | $170k | $170k |
Highest paying role
Product Engineer
$170k
Normalized to USD per year
Growth
Hiring Momentum
54/100
Stable
Open positions
4
0 opened / 0 closed in 30 days
ATS activity
Every ~3 hours
Today
Hiring Dynamics
+100%
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.
Backend
75% ▼
Operations
25% ▼
Activity Timeline
Added Zapier to stack
Sep 2026
Added Anthropic to stack
Aug 2026
Offices
Where the company hires and what each office is for
Cities
1
Hiring now
1
Countries
1
Busiest office
San Francisco
San Francisco
United States
HQ
4 open roles 75% engineering 1-10 est. staff $137k Avg Salary
hires for Backend, Operations
Jobs
$120k per year • In office • Internship • San Francisco
Cursor
Claude Code
FastAI
AI Agents
PyTorch
Anthropic
DevOps
Datadog
Cloudflare
Apply
$120k per year • In office • Full-Time • San Francisco
Cursor
Claude Code
FastAI
AI Agents
PyTorch
Anthropic
DevOps
Datadog
Cloudflare
Apply
$170k per year • In office • Full-Time • 3+ years exp • San Francisco
Cursor
Claude Code
FastAI
AI Agents
PyTorch
Anthropic
DevOps
Datadog
Cloudflare
Apply