Jobs
Required Skills
Rust
Systems Design
Linux
Performance Optimization
Distributed Systems
Perplexity AI is seeking a talented Search Rust Engineer to join our rapidly growing team, driving innovation in AI-powered search experiences. As a Search Rust Engineer, your main mission will be to relentlessly optimize performance - squeezing every millisecond of latency from our search stack, while implementing robust, scalable, and reliable systems.
Responsibilities:
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Architect, build, and optimize ultra-low-latency search infrastructure using Rust
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Profile and instrument services, continuously driving down response times at scale
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Develop and maintain distributed backend components powering real-time search and retrieval
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Collaborate closely with product and infrastructure teams to design systems that deliver best-in-class query performance
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Apply advanced concurrency, memory management, and network programming patterns for maximum throughput
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Monitor, debug, and tune production workloads to ensure reliability under demanding traffic
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Contribute to code reviews and mentor peers in high-efficiency Rust development
Requirements:
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Deep expertise in Rust programming, especially for backend/search systems
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Experience profiling and tuning high-load, low-latency distributed services
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Strong understanding of systems design, Linux internals, and performance debugging
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Background in cloud infrastructure - preferably AWS - and container orchestration
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Track record of driving efficiency for search or data-intensive products
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Ability to thrive in fast-paced environments and own critical product code
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Passion for benchmarking, instrumentation, and continuous performance improvement
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About Perplexity AI

Perplexity AI
Series BPerplexity AI, Inc., or simply Perplexity, is an American privately held software company offering a web search engine that processes user queries and synthesizes responses.
51-200
Employees
San Francisco
Headquarters
$1B
Valuation
Reviews
4.0
1 reviews
Work Life Balance
3.0
Compensation
3.0
Culture
3.0
Career
3.5
Management
3.0
70%
Recommend to a Friend
Pros
Helpful tool for research and analysis
Useful for job application preparation
Effective for complex marketing challenges
Cons
Limited feedback provided
No specific criticisms mentioned
Insufficient detail on potential drawbacks
Salary Ranges
28 data points
Junior/L3
Junior/L3 · LLM Teacher
1 reports
$101,920
total / year
Base
$78,400
Stock
-
Bonus
-
$101,920
$101,920
Interview Experience
1 interviews
Difficulty
4.0
/ 5
Duration
14-28 weeks
Experience
Positive 0%
Neutral 0%
Negative 100%
Interview Process
1
Application Review
2
HR Screen
3
Take-home Marketing Challenge
4
Hiring Manager Interview
5
Panel Interview
6
Offer
Common Questions
Digital Marketing Strategy
Campaign Performance Analysis
Behavioral/STAR
Technical Marketing Knowledge
Case Study
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