ML Engineer – LLMs & Production Systems (Hybrid/Part‑Time)

ContentBuffer; San Francisco (CA); Hybrid; USD 217,000 - 303,000; Full time

Benefits offered by this job

Comprehensive Healthcare Benefits and

401k with Employer Match

Global Benefit programs

Family Planning Support

Gender-Affirming Care

Mental Health & Coaching Benefits

Flexible Vacation & Paid Volunteer

Generous Paid Parental Leave

Job summary

Reddit is seeking a machine learning engineer to design, build, and deploy industrial‑level ML models for ad ranking, bidding, and optimization. You will own the ML lifecycle end‑to‑end, from ideation to scalable serving systems and production maintenance.

Collaborating with product managers, data scientists, and engineers, you will transform complex business problems into effective ML solutions while improving reliability with monitoring and automated retraining pipelines in a hybrid SF office

Qualifications

• Master’s degree in Computer Science, Robotics, Engineering (or related quantitative field) with two years of related experience.
• Experience delivering ML models end-to-end in production.
• Knowledge of data governance and ML lifecycle practices.

Responsibilities

• Design and deploy ML models for ad ranking, bidding, and optimization.
• Own the ML lifecycle from ideation to production serving systems.
• Engineer features from diverse data for model performance improvements.
• Build robust monitoring, alerting, and retraining pipelines.

Skills

PII data handling

Kafka infrastructure

Data governance

Batch data pipeline

SLOs

ML models

XGBoost

LLM

GCP

Education

Master’s degree in CS/Robotics/Engineering

Tools

Google Cloud Platform

Graph service

Job description

Reddit is seeking a machine learning engineer to design, build, and deploy industrial‑level ML models for ad ranking, bidding, and optimization. You will own the ML lifecycle end‑to‑end, from ideation to scalable serving systems and production maintenance.

Collaborating with product managers, data scientists, and engineers, you will transform complex business problems into effective ML solutions while improving reliability with monitoring and automated retraining pipelines in a hybrid SF office