ML Engineer, Real-Time Recommendations (Onsite)

TikTok USDS JV; San Jose (CA); Presencial; USD 123 000 - 259 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Medical, dental, and vision coverage

401(k) with company match

Parental leave

Disability coverage

Life insurance

Wellbeing benefits

Holidays (10) per year

Sick days (10) per year

Paid Personal Time (PT)

Resumo da oferta

TikTok USDS Joint Venture LLC in San Jose, CA is hiring for ML engineers to design and operate large-scale ML systems powering recommendations and e-commerce. You will build robust MLOps pipelines, real-time data streams, and feature stores to support production models serving tens of millions of users.

You will collaborate with researchers and platform teams to push models from prototype to zero-downtime production deployments, with onsite work as part of a dynamic team that is evolving toward

Qualificações

• Bachelor’s degree in Computer Science or related field.
• Strong programming and problem-solving skills.
• Familiarity with MLOps concepts: data preprocessing, Docker, model tracking, and workflow orchestration.

Responsabilidades

• Design, build, and maintain robust MLOps pipelines and CI/CD for ML.
• Architect high-throughput real-time streaming data pipelines and feature stores.
• Optimize candidate generation and ranking models for production serving (CTR/CVR).
• Improve inference performance with quantization, parallel computation, and latency reduction.
• Create automated monitoring, debugging, and visualization for feature drift and model performance.
• Collaborate with researchers and platform teams to move models to zero-downtime production.

Conhecimentos

MLOps

CI/CD

Docker

C++

CUDA

TensorRT

CTR

CVR

Formação académica

Bachelor’s degree in Computer Science

Ferramentas

Docker

C++

CUDA

TensorRT

Descrição da oferta de emprego

TikTok USDS Joint Venture LLC in San Jose, CA is hiring for ML engineers to design and operate large-scale ML systems powering recommendations and e-commerce. You will build robust MLOps pipelines, real-time data streams, and feature stores to support production models serving tens of millions of users.

You will collaborate with researchers and platform teams to push models from prototype to zero-downtime production deployments, with onsite work as part of a dynamic team that is evolving toward