Senior Machine Learning Engineer, Ads Foundational Representations

Reddit Reddit · Consumer · London, United Kingdom · Ads Engineering

Senior ML Engineer role focused on developing foundational representations and embeddings for Reddit's Ads platform, leveraging multimodal content, user behavior, knowledge graphs, and LLMs to improve ranking and targeting outcomes. The role involves the full ML project lifecycle, from data processing and model development to evaluation and deployment.

What you'd actually do

  1. Developing new or iterating on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models.
  2. Working with local and 3rd-party LLMs/VLMs: extract representations, develop evaluation methodologies, prompt tune and fine-tune large models to build state-of-the-art embeddings.
  3. Building data processing and inference pipelines for the models we develop.
  4. Qualitative and quantitative evaluation of the various features we develop, end-to-end experimentation from internal benchmarks to downstream recommender system offline metrics to online experiments.
  5. Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management.

Skills

Required

  • PyTorch or TensorFlow
  • NLP
  • CV
  • ML model deployment
  • ML model evaluation
  • ML model testing
  • ML model training
  • ML model design
  • feature engineering
  • embedding models

Nice to have

  • Python
  • Pytorch
  • Airflow
  • BigQuery
  • Ray
  • k8s
  • kafka
  • GCP
  • Ads domain
  • Search/Recommender systems
  • LLMs
  • fine-tuning LLMs
  • building LLMs
  • Tech leadership
  • mentoring junior engineers
  • leading complex projects

What the JD emphasized

  • full lifecycle of designing, training, evaluating, testing, and deploying industry-level models
  • Experience building NLP or CV models and integrating them at scale
  • Experience developing complex features/embeddings for downstream models
  • Hands-on experience with using/fine-tuning/building LLMs

Other signals

  • building embeddings
  • multimodal content embeddings
  • LLM-based representations
  • user intent modeling
  • full-cycle execution of ML projects