Machine Learning Engineer (Answers Relevance)

Join Reddit as a Senior or Staff Machine Learning Engineer, where you'll lead ML projects from idea to production, build and improve large-scale ML systems, and mentor engineers. You'll have a significant impact on product and technical roadmaps, and enjoy comprehensive health benefits, flexible vacation, and personal and professional development funds.

• Lead ML projects from idea to production, partnering with Product, Data Science, and Engineering across recommendations, search, messaging, and AI systems.
• 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, working with partner teams to shape product and technical roadmaps.

• SOTA large scale RecSys
• Agentic AI, such as tool calls, MCPs, multi-agent systems, or orchestration
• RAG systems, retrieval methods, LLM evaluation, or production LLM features
• Recommendations, search, content understanding, or large-scale data and ML infrastructure
• Improving grounding, instruction following, and hallucination; evaluating retrieval and generation quality
• You don’t need experience in every area listed below. A background in a few may make you a great fit!
• Defining product success metrics, feedback loops, and evaluation approaches, including human review
• Self-hosted open-weight models such as Qwen or Llama; prompt engineering, fine-tuning, post-training, or prompt optimization
• Foundational retriever models
• Scaling LLM systems, including latency, caching, and cost; safety measures for prompt injection and unsafe inputs
• Experience writing production-quality code, including testing, evaluation, and monitoring; Python experience required, Golang experience a plus
• Broad knowledge of ML fundamentals and experience applying them in production; expert-level modeling depth isn’t required
• 4–12 years of experience building, deploying, and operating ML systems in production
• Experience developing and productionizing models with TensorFlow, PyTorch, or Hugging Face Transformers
• 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