Senior ML Engineer

About IntelePeer

IntelePeer is a healthcare-focused AI communications platform that powers AI voice agents and intelligent workflow automation for ambulatory care groups, all specialty healthcare verticals, health systems, and payers. Our AI Agent suite and SmartFlow platform are deployed at scale across some of the nation's most complex healthcare organizations — handling millions of patient interactions annually for scheduling, care coordination, billing inquiry, and more. We build AI that talks to real patients and produces real outcomes, and we need people who take that responsibility seriously.

Job Summary

IntelePeer is building AI-native communications products and we need an ML engineer who gets their hands dirty. This is not a research role — you will own the full lifecycle of machine learning systems: designing training pipelines, fine-tuning and aligning large language models, optimizing inference, and shipping models that run reliably in production. You will work alongside our AI Engineering team to push the capabilities of our platform and 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 techniques such as LoRA, QLoRA, and full fine-tuning; apply PEFT strategies to balance performance and compute cost.
• Implement and experiment with reinforcement learning from human feedback (RLHF) workflows, including PPO (Proximal Policy Optimization) and GRPO (Group Relative Policy Optimization) for model alignment and preference optimization.
• Host, serve, and optimize LLMs in production using inference frameworks such as vLLM, Text Generation Inference (TGI), Triton Inference Server, or ONNX Runtime.
• Evaluate, benchmark, and select inference providers (e.g., Together AI, Fireworks, Groq, Replicate, AWS Bedrock, Azure OpenAI) based on latency, cost, throughput, and model capability trade-offs.
• Build and maintain embedding pipelines — generate, index, and retrieve dense embeddings using vector databases (Pinecone, pgvector, Weaviate, or similar) for RAG and semantic search applications.
• Implement and expose ML capabilities via Model Context Protocol (MCP) — enabling AI agents to call model-backed tools in a structured, context-aware manner.
• Perform rigorous data analysis and processing: clean, transform, and curate datasets for training, fine-tuning, and evaluation; build data quality and validation pipelines.
• Develop robust model evaluation frameworks — define metrics, build eval harnesses, run A/B experiments, and track regressions across model versions.
• Collaborate with software engineers to integrate ML systems into product features via FastAPI services; ensure models are observable, versioned, and maintainable in production.

Supervisory Duties

• This is an IC role

Minimum Education And Experience

• Bachelor's in computer science or statistics
• 3–8+ years of hands-on ML engineering experience with a strong production track record
• Deep understanding of core ML concepts: neural network architectures (transformers, attention mechanisms), loss functions, optimization algorithms, regularization, and model evaluation
• Practical experience fine-tuning LLMs (LoRA, QLoRA, PEFT, instruction tuning, DPO) on custom datasets using frameworks such as Hugging Face Transformers, TRL, or Axolotl
• Hands-on experience with RL-based alignment techniques — specifically PPO and GRPO — for reward modeling, preference optimization, and RLHF pipelines
• Experience hosting and serving LLMs: vLLM, TGI, Triton, or similar; understanding of model quantization (GPTQ, AWQ, int4/int8), batching strategies, and throughput optimization
• Working knowledge of major inference vendors and cloud AI APIs; ability to evaluate and select providers based on cost, latency, and capability benchmarks
• Proficiency in embedding models (sentence-transformers, OpenAI embeddings, or equivalent) and vector search infrastructure for RAG pipelines
• Understanding of Model Context Protocol (MCP) and how to expose ML functionality as structured tools for agentic systems

Key Competencies

• Experience with distributed training frameworks (DeepSpeed, FSDP, Megatron-LM) for multi-GPU or multi-node training runs
• Familiarity with MLOps tooling: MLflow, Weights & Biases, DVC, or similar for experiment tracking, model registry, and pipeline orchestration
• Knowledge of synthetic data generation techniques for augmenting fine-tuning datasets
• Exposure to multimodal models (vision-language, speech-language) or voice/speech AI systems
• Contributions to open-source ML projects or published research (papers, blog posts, or technical write-ups)