Senior ML Engineer

IntelePeer

RemotePosted 28 июля 2026 г.

About the role

IntelePeer is a healthcare-focused AI communications platform that powers AI voice agents and intelligent workflow automation for ambulatory care groups, health systems, and payers. We are seeking a Senior ML Engineer who will own the full lifecycle of machine learning systems in production, including designing training pipelines, fine-tuning and aligning large language models, optimizing inference, and shipping models that run reliably at scale. This is not a research role; you will work with the AI Engineering team to deliver measurable impact.

Senior ML Engineer

IntelePeer

RemotePosted 28 июля 2026 г.

About the role

IntelePeer is a healthcare-focused AI communications platform that powers AI voice agents and intelligent workflow automation for ambulatory care groups, health systems, and payers. We are seeking a Senior ML Engineer who will own the full lifecycle of machine learning systems in production, including designing training pipelines, fine-tuning and aligning large language models, optimizing inference, and shipping models that run reliably at scale. This is not a research role; you will work with the AI Engineering team to deliver measurable impact.

Responsibilities

Design, implement, and maintain end-to-end ML training pipelines from raw data ingestion and preprocessing through model training, evaluation, and deployment.
Fine-tune large language models using LoRA, QLoRA, and PEFT techniques; apply RLHF workflows such as PPO and GRPO for alignment and preference optimization.
Host, serve, and optimize LLMs in production using inference frameworks like vLLM, Text Generation Inference, Triton, or ONNX Runtime.
Evaluate and select inference providers based on latency, cost, throughput, and capabilities.
Build and maintain embedding and vector search pipelines for retrieval-augmented generation and semantic search (Pinecone, pgvector, Weaviate, etc.).
Expose ML capabilities through a structured Model Context Protocol (MCP) to enable AI agents to call model-backed tools.
Prepare data through cleaning, transformation, and validation pipelines; ensure data quality for training and evaluation.
Develop evaluation frameworks, define metrics, run A/B experiments, and tr

Responsibilities

Design, implement, and maintain end-to-end ML training pipelines from raw data ingestion and preprocessing through model training, evaluation, and deployment.
Fine-tune large language models using LoRA, QLoRA, and PEFT techniques; apply RLHF workflows such as PPO and GRPO for alignment and preference optimization.
Host, serve, and optimize LLMs in production using inference frameworks like vLLM, Text Generation Inference, Triton, or ONNX Runtime.
Evaluate and select inference providers based on latency, cost, throughput, and capabilities.
Build and maintain embedding and vector search pipelines for retrieval-augmented generation and semantic search (Pinecone, pgvector, Weaviate, etc.).
Expose ML capabilities through a structured Model Context Protocol (MCP) to enable AI agents to call model-backed tools.
Prepare data through cleaning, transformation, and validation pipelines; ensure data quality for training and evaluation.
Develop evaluation frameworks, define metrics, run A/B experiments, and tr

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