Machine Learning Engineer, Answers Relevance

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .

At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From “How do I fix a leaky faucet?” to “What should I watch next?”, the Answers Relevance team leverages AI to connect people with the real-world wisdom of Reddit communities. We’re hiring Senior and Staff Machine Learning Engineers to make Answers smarter, more relevant, and more helpful. Our SOTA AI systems helps surface the right conversations, but the heart of Answers is still people helping people ~ Redditors sharing what they know with each other!

What You’ll Work On You’ll lead ML projects from idea to production, partnering with Product, Data Science, and Engineering across recommendations, search, messaging, and AI systems.

You don’t need experience in every area listed below. A background in a few may make you a great fit!

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

Required Qualifications

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