Backend Engineer, Recommendation Architecture

TikTok; San Jose, Northern (CA, KY); Hybrid; USD 128,000 - 317,000; Full time

Benefits offered by this job

Health insurance

401(k) with company match

Paid parental leave

Disability insurance

Life insurance

Wellbeing benefits

Paid holidays

Paid time off

Job summary

TikTok is hiring for a role contributing to the design and optimization of the recommendation system. You will build and maintain high-availability services, create robust data pipelines, and push for extreme performance improvements across storage, computing, and application layers.

You will collaborate with ML engineers to productionize models efficiently and safely, participate in capacity planning, and help ensure reliable monitoring and disaster recovery for the platform.

Qualifications

• Bachelor's degree in Computer Science or related discipline.
• Strong programming skills with good code design in Go, C++, Java or Python.
• Solid understanding of data structures, algorithms, OS, networking and distributed systems.

Responsibilities

• Participate in the design of the recommendation system to enhance development efficiency, performance, scalability, and recommendation effectiveness.
• Build and maintain high-performance, high-availability online services for the TikTok recommendation system.
• Build efficient and reliable data pipelines for candidate generation, profile generation, and training-example generation.
• Pursue extreme performance optimization in storage, computing, or application layers to improve latency and throughput.
• Troubleshoot production issues and ensure operational stability including capacity planning and monitoring.
• Collaborate with ML engineers to productionize models efficiently and safely.

Skills

Go

C++

Java

Python

Distributed systems

Data structures

Networking

Operating systems

Education

Bachelor's degree in Computer Science or related discipline

Tools

RPC frameworks

Redis

Kafka

Job description

TikTok is hiring for a role contributing to the design and optimization of the recommendation system. You will build and maintain high-availability services, create robust data pipelines, and push for extreme performance improvements across storage, computing, and application layers.

You will collaborate with ML engineers to productionize models efficiently and safely, participate in capacity planning, and help ensure reliable monitoring and disaster recovery for the platform.