Machine Learning Engineer, Answers Relevance

The Machine Learning Engineer will develop and scale advanced AI systems that help users discover relevant, trustworthy answers from large collections of online conversations. In this remote role, you will build and improve retrieval, ranking, recommendation, and large language model (LLM) capabilities that connect people with useful community knowledge. You will collaborate with Product, Data Science, and Engineering teams to take machine learning projects from initial concepts through production deployment. Your work will help improve answer relevance, retrieval quality, system efficiency, and overall user experience. You will also evaluate emerging AI techniques, establish meaningful success metrics, and contribute to responsible AI development. This opportunity is ideal for an experienced ML engineer who enjoys solving complex technical challenges and delivering measurable impact through production-grade machine learning systems.

• 4–12 years of experience building, deploying, and operating machine learning systems in production.

• Broad understanding of machine learning fundamentals and demonstrated ability to apply them to practical, production-scale problems.

• Experience developing and productionizing machine learning models using frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.

• Strong Python programming skills and experience writing reliable, production-quality code, including testing, evaluation, and monitoring. Golang experience is a plus.

• Experience with core machine learning systems and workflows, including data pipelines, feature engineering, model training, inference, serving, and production monitoring.

• Familiarity with one or more relevant areas, such as recommendation systems, search, content understanding, large-scale ML infrastructure, retrieval and ranking, embeddings, or foundational retriever models.

• Experience with LLM-based applications, RAG systems, retrieval techniques, LLM evaluation, or production language model features is advantageous.

• Knowledge of open-weight language models, prompt engineering, fine-tuning, post-training, or prompt optimization is beneficial.

• Familiarity with agentic AI, tool calling, multi-agent systems, or orchestration frameworks is a plus.

• Understanding of model quality evaluation, hallucination reduction, grounding, instruction following, and responsible AI practices is desirable.

• Experience optimizing large-scale AI systems for latency, caching, cost, scalability, or reliability is an advantage.

• Strong analytical and problem-solving abilities, with the capacity to assess new approaches and translate technical findings into practical improvements.

• Excellent communication and collaboration skills, with a track record of working across disciplines and delivering measurable product impact.

• Ability to take ownership of technical projects, contribute to architectural decisions, and help guide the development of scalable ML solutions.

• Willingness to mentor colleagues and contribute to engineering best practices and long-term technical planning.

• Must be based in the United States and authorized to work in the country, subject to the hiring organization's employment eligibility requirements.