Senior Machine Learning Engineer, Customer Support Engineering
New
Based in the United StatesFull-TimeSenior
SalaryCompetitive compensation including base salary, bonus, and equity
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonMachine LearningSoftware EngineeringLLMMLOps
Requirements
- Master’s or PhD in Computer Science, Machine Learning, AI, or related field, or equivalent industry experience.
- 6+ years of hands-on experience building and shipping ML/AI systems at scale in production environments.
- Strong expertise in large language models, including pretraining, fine-tuning (SFT, RLHF, GRPO), prompt engineering, and evaluation methodologies.
- Proven experience designing and implementing agentic AI systems (e.g., ReAct, LangGraph, AutoGen, or similar frameworks).
- Deep understanding of RAG architectures, retrieval systems, and LLM-based reasoning pipelines.
- Strong software engineering skills with experience in building scalable, reliable ML infrastructure and services.
- Experience with MLOps practices, model deployment, monitoring, and performance optimization in production environments.
- Strong cross-functional communication skills with ability to collaborate across engineering, product, and design teams.
- Ability to thrive in fast-paced, research-driven environments where ambiguity and experimentation are common.
- Passion for AI innovation and improving real-world customer experiences through applied machine learning.
Responsibilities
- Design, build, and optimize advanced ML systems powering customer support experiences, including chat and voice AI assistants.
- Develop and productionize agentic AI architectures involving multi-agent orchestration, tool use, reasoning pipelines, and autonomous decision-making systems.
- Build and improve LLM-based solutions including fine-tuning (SFT, RLHF, GRPO), prompt engineering, RAG systems, and evaluation frameworks.
- Lead end-to-end development of ML features from early research exploration to scalable production deployment.
- Collaborate cross-functionally with Product, Design, and Engineering teams to translate customer support needs into robust AI solutions.
- Improve model performance through experimentation, feedback loops, evaluation automation, and guardrail design.
- Contribute to ML infrastructure, model serving systems, and MLOps best practices to ensure reliability and scalability.
- Stay current with emerging AI research and actively share insights to elevate team capabilities and technical direction.
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