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# Machine Learning Engineer, Answers Relevance

- **Company:** reddit Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $216,700.0 - $322,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Content Analysis, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Qwen, Agentic-AI, HuggingFace, Production Code, Machine Learning Operations, Evaluation of Large Language Models, Data Pipelines, Golang
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_5f4fdcd9c5279a726ee1732f674a1a70

## About the Role

* 4-12 years of experience building, deploying, and operating ML systems in production
 * Broad knowledge of ML fundamentals and experience applying them in production; expert-level modeling depth isn't required
 * Experience developing and productionizing models with TensorFlow, PyTorch, or Hugging Face Transformers
 * Experience writing production-quality code, including testing, evaluation, and monitoring; Python experience required, Golang experience a plus
 * Experience with ML systems such as data pipelines, feature engineering, model training and serving, and production monitoring
 * Strong communication and collaboration skills, and a record of delivering measurable product impact

## Description

* Recommendations, search, content understanding, or large-scale data and ML infrastructure
 * SOTA large scale RecSys
 * Foundational retriever models
 * RAG systems, retrieval methods, LLM evaluation, or production LLM features
 * Agentic AI, such as tool calls, MCPs, multi-agent systems, or orchestration
 * Self-hosted open-weight models such as Qwen or Llama; prompt engineering, fine-tuning, post-training, or prompt optimization
 * Improving grounding, instruction following, and hallucination; evaluating retrieval and generation quality
 * Scaling LLM systems, including latency, caching, and cost; safety measures for prompt injection and unsafe inputs
 * Defining product success metrics, feedback loops, and evaluation approaches, including human review

What You'll Do

 * Build and improve large-scale ML systems, including retrieval and ranking, embeddings, and LLM-powered features
 * Set technical direction and deliver measurable user and business impact
 * Work with partner teams to shape product and technical roadmaps
 * Evaluate new AI/ML approaches and support responsible LLM development
 * Mentor engineers and contribute to long-term ML planning