Machine Learning Engineer, Answers Relevance

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Machine Learning Engineer, Answers Relevance based in the United States. 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.

Design, develop, deploy, and optimize large-scale machine learning systems, including information retrieval, ranking, embeddings, recommendation systems, and LLM-powered features. Lead machine learning projects from ideation through production, ensuring solutions deliver measurable improvements in answer relevance, user experience, and business outcomes. Develop and improve retrieval and ranking models that surface useful, contextually relevant information from large-scale content collections. Build and optimize retrieval-augmented generation (RAG) systems, foundational retriever models, and other AI-powered features that improve the quality and usefulness of generated answers. Explore and implement advanced AI approaches, including large-scale recommendation systems, agentic AI, multi-agent orchestration, tool calling, and model context protocol (MCP) integrations where appropriate. Work with open-weight language models, including models such as Qwen and Llama, applying prompt engineering, fine-tuning, post-training, and prompt optimization techniques as needed. Improve model grounding, instruction following, and factual reliability while reducing hallucinations and strengthening retrieval and generation quality. Evaluate new machine learning methods and establish robust evaluation frameworks, feedback loops, product success metrics, and human-review processes. Optimize production LLM systems for latency, caching, cost efficiency, scalability, and reliability while implementing safeguards against prompt injection and unsafe inputs. Collaborate with Product, Data Science, and Engineering partners to define technical direction, prioritize initiatives, and shape product and engineering roadmaps. Build and maintain reliable ML infrastructure, including data pipelines, feature engineering workflows, model training and serving systems, and production monitoring. Write high-quality, maintainable production code with appropriate testing, evaluation, and monitoring practices. Mentor fellow engineers, share technical expertise, and contribute to long-term machine learning strategy and planning. Requirements: 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. Benefits: Salary: Annual base salary range of $216,700–$322,000 USD, depending on role level, relevant experience, skills, qualifications, and other job-related factors. Equity compensation: Eligibility for restricted stock units (RSUs), with additional commission eligibility depending on the position offered. Healthcare coverage: Comprehensive healthcare benefits, including medical, dental, and vision insurance. Retirement savings: 401(k) plan with employer matching contributions. Income protection: Income replacement programs for eligible employees. Flexible time off: Vacation benefits and paid volunteer time off. Parental leave: Generous paid parental leave. Family support: Family planning benefits and resources. Mental health and wellbeing: Mental health support, coaching, and related wellbeing resources. Professional development: Benefits and resources to support continuous learning and career growth. Flexible work support: Global benefits designed to accommodate different lifestyles, including workspace and caregiving support. Remote work: Remote opportunity within the United States. Inclusive workplace: Commitment to equal employment opportunity, accessibility, and reasonable accommodations throughout the recruitment process.