Edge AI/ML Engineer — Distributed Inference & RAG

Expression; Washington (District of Columbia); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

401k matching

Medical/dental/vision insurance

Education reimbursement

Life insurance

Generous PTO and holiday leave

Onsite gym facility and trainer

Commuter Benefits Plan

In-office Cold Brew Coffee

Job summary

Expression seeks an experienced AI/ML Engineer to design, optimize, and evaluate machine learning capabilities for heterogeneous edge computing platforms. You will work with software engineers to develop AI pipelines enabling intelligent signal characterization, data prioritization, and distributed inference in constrained environments.

The ideal candidate has strong expertise in applied machine learning, modern LLM technologies, edge AI optimization, and production deployment of AI systems.

Qualifications

• Bachelor degree in Computer Science, AI, Data Science, Electrical Engineering, or related; advanced degree preferred.
• 5–8+ years of professional experience developing production AI or machine learning applications.
• Strong Python programming experience.
• Experience with PyTorch.
• Experience deploying LLMs in production environments.
• Experience with LangGraph, LangChain, CrewAI, Semantic Kernel, or similar orchestration frameworks.
• Experience implementing Retrieval-Augmented Generation (RAG).
• Experience with vector databases and semantic search.
• Experience deploying AI models on edge or resource-constrained devices.
• Experience with model optimization techniques including quantization, pruning, or distillation.
• Experience designing evaluation frameworks for AI systems.
• Experience with Docker and cloud-native AI deployment.
• Excellent communication and collaboration skills.

Responsibilities

• Design and implement AI capabilities supporting intelligent data characterization, classification, prioritization, and decision support.
• Evaluate, optimize, and deploy open-weight foundation models appropriate for resource-constrained edge environments.
• Develop efficient inference pipelines supporting heterogeneous compute environments ranging from embedded processors to workstation-class systems.
• Implement Retrieval-Augmented Generation (RAG), semantic search, and knowledge retrieval capabilities where appropriate.
• Design AI orchestration workflows supporting distributed inference across multiple edge devices.
• Develop evaluation methodologies for AI accuracy, latency, resource utilization, and operational performance.
• Implement model monitoring, observability, testing, and automated evaluation frameworks.
• Collaborate with software engineers to integrate AI models into production software platforms.
• Optimize models using quantization, pruning, distillation deployment technologies.
• Support experimentation involving multimodal data sources, sensor-derived features, and structured mission data.
• Develop AI governance practices including model evaluation, explainability, responsible AI, and secure deployment.
• Document model development, evaluation results, and technical recommendations.
• Support customer demonstrations and prototype evaluations.

Skills

Python

PyTorch

LLM deployment

Edge computing

RAG

Distributed inference

Communication

Collaboration

Education

Bachelor's degree in Computer Science or related

Advanced degree preferred

Tools

Docker

LangGraph

LangChain

Semantic Kernel

Vector databases

Cloud deployment

Job description

Expression seeks an experienced AI/ML Engineer to design, optimize, and evaluate machine learning capabilities for heterogeneous edge computing platforms. You will work with software engineers to develop AI pipelines enabling intelligent signal characterization, data prioritization, and distributed inference in constrained environments.

The ideal candidate has strong expertise in applied machine learning, modern LLM technologies, edge AI optimization, and production deployment of AI systems.