Senior ML Engineer

🕒 Yesterday

⛰️ Colorado, Florida – Remote

⛰️ Colorado – Remote 🐊 Florida – Remote

💵 $180k - $190k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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IntelePeer

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2003

🤖 Artificial Intelligence

📡 Telecommunications

🔌 API

💰 $140M Private Equity Round - IntelePeer on 2024-07

Artificial Intelligence • Telecommunications • API

IntelePeer is a provider of rapidly deployable communications solutions and a Conversational AI Platform that enhances customer communications and support. The company offers Agentic AI capabilities that integrate with existing infrastructure, and provides no-code templates, low-code options, co-creation services, and developer APIs to accelerate time-to-value for businesses. IntelePeer targets enterprise and B2B customers seeking programmable communications, AI-driven interactions, and easy integration into their systems.

📋 Description

• 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.

🎯 Requirements

• Bachelors 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.

🏖️ Benefits

• Unlimited Vacation for exempt employees • Paid Holidays • Competitive medical, dental & vision insurance for employees and their dependents • 401K Retirement Plan • Stock Options • Company-paid life insurance • Health & Flexible Savings Accounts • Cell phone, gym, and internet reimbursement • Paid Parental Leave • Tuition Reimbursement • Employee Assistance Program (EAP) • Free snacks (Denver, and or Fort Lauderdale) • Fun events (virtual and in-person)