Machine Learning Engineer, Answers Relevance

New

JobgetherMachine learning

Remote opportunity within the United States.Full-TimeMiddle

SalaryAnnual base salary range of $216,700–$322,000 USD

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Job Details

Experience: 4–12 years of experience building, deploying, and operating machine learning systems in production.
Required Skills: PythonMachine LearningPyTorchTensorflow

Have 4–12 years of experience building, deploying, and operating machine learning systems in production.
Understand machine learning fundamentals and apply them to practical, production-scale problems.
Have experience developing and productionizing models with TensorFlow, PyTorch, or Hugging Face Transformers.
Have strong Python programming skills and experience writing reliable production code, including testing, evaluation, and monitoring.
Have experience with machine learning workflows such as data pipelines, feature engineering, model training, inference, serving, and production monitoring.
Have familiarity with recommendation systems, search, content understanding, large-scale ML infrastructure, retrieval and ranking, embeddings, or foundational retriever models.
Have experience with LLM applications, RAG systems, retrieval techniques, LLM evaluation, or production language model features.
Be familiar with open-weight language models, prompt engineering, fine-tuning, post-training, or prompt optimization.
Understand model quality evaluation, grounding, instruction following, hallucination reduction, or responsible AI practices.
Be able to assess technical approaches, own projects, contribute to architectural decisions, and collaborate across disciplines.
Be willing to mentor colleagues and contribute to engineering practices and long-term technical planning.

Design, develop, deploy, and optimize large-scale machine learning systems for retrieval, ranking, embeddings, recommendations, and LLM-powered features.
Lead machine learning projects from ideation through production and measure improvements in answer relevance and user experience.
Develop retrieval and ranking models that surface useful information from large-scale content collections.
Build and optimize retrieval-augmented generation systems, retriever models, and other AI-powered features.
Explore and implement recommendation systems, agentic AI, multi-agent orchestration, tool calling, and MCP integrations where appropriate.
Improve model grounding, instruction following, factual reliability, and retrieval and generation quality.
Evaluate machine learning methods and establish evaluation frameworks, feedback loops, product metrics, and human-review processes.
Optimize production LLM systems for latency, caching, cost, scalability, and reliability, and implement safeguards against prompt injection and unsafe inputs.
Build and maintain ML infrastructure, including data pipelines, feature engineering workflows, model training and serving systems, and production monitoring.
Collaborate with Product, Data Science, and Engineering teams, contribute to technical direction, and mentor fellow engineers.

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