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Edge Ai Jobs (NOW HIRING)

Rite-Hite

Systems Architect - Edge AI/ML

Milwaukee, WI · On-site

$238K/yr

They are seeking a Systems Architect, Edge AI/ML to define and guide the architecture for AI and machine learning solutions deployed on edge platforms, ensuring scalability, reliability, and security.

Ambarella

$230K - $265K/yr

AI Vision Processors For Edge Applications Our solutions make cameras smarter by extracting valuable data from high-resolution video streams. Job Title: Edge AI Silicon Product Marketing Director ...

ARM

Senior Product Manager, Edge AI CPU

Austin, TX · On-site

$125K - $165K/yr

Arm is enabling the next generation of Edge AI by combining industry-leading compute platforms with optimized implementation technologies that accelerate customer innovation. As AI workloads continue ...

ARM

Senior Product Manager, Edge AI CPU

Austin, TX · On-site

$125K - $165K/yr

Arm is enabling the next generation of Edge AI by combining industry-leading compute platforms with optimized implementation technologies that accelerate customer innovation. As AI workloads continue ...

Byrne Dairy

Edge AI & Sensing Software Engineer

Rome, NY · On-site

$88K - $158K/yr

AIS's Cyber AI team develops and delivers cutting-edge AI/ML, Cyber and Intelligence capabilities. We're looking to hire engineers at different experience levels. These onsite positions are located ...

New

SOLX HOLDINGS LLC

PR · On-site

Build the platform that runs SEVN -- the software infrastructure that deploys, monitors, and scales vision models across the fleet of edge nodes on the line. What you'll do * Build and operate the ...

Sonatus

Senior Staff AI Engineer, Edge AI

Sunnyvale, CA · On-site

$122K - $168K/yr

Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM, Trees) to process unstructured application logs, kernel traces, and multi-modalities. * Integrating ML flows, including cloud-based ...

Sonatus

Senior Staff AI Engineer, Edge AI

San Jose, CA · Hybrid

$122K - $168K/yr

Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM, Trees) to process unstructured application logs, kernel traces, and multi-modalities. * Integrating ML flows, including cloud-based ...

Sonatus

Senior Staff AI Engineer, Edge AI

Mountain View, CA · Hybrid

$123K - $169K/yr

Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM, Trees) to process unstructured application logs, kernel traces, and multi-modalities. * Integrating ML flows, including cloud-based ...

Sonatus

Senior Staff AI Engineer, Edge AI

Sunnyvale, CA · Hybrid

$122K - $168K/yr

Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM, Trees) to process unstructured application logs, kernel traces, and multi-modalities. * Integrating ML flows, including cloud-based ...

Sonatus

Senior Staff AI Engineer, Edge AI

San Francisco, CA · Hybrid

$123K - $169K/yr

Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM, Trees) to process unstructured application logs, kernel traces, and multi-modalities. * Integrating ML flows, including cloud-based ...

Sonatus

Senior Staff AI Engineer, Edge AI

Sunnyvale, CA · On-site

$124K - $170K/yr

Build and train AI Edge models (e.g., Transformers, LLM, CNN, LSTM, Trees) to process unstructured application logs, kernel traces, and multi-modalities. * Integrating ML flows, including cloud-based ...

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How much do edge ai jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for edge ai in the United States is $20.30, according to ZipRecruiter salary data. Most workers in this role earn between $13.46 and $24.04 per hour, depending on experience, location, and employer.

What is the difference between Edge Ai vs Data Scientist?

AspectEdge Ai
Required CredentialsTypically a degree in computer science, electrical engineering, or related fields; certifications in AI or machine learning are common
Work EnvironmentPrimarily involves working with embedded systems, IoT devices, and hardware in diverse locations
Employer & Industry UsageUsed by tech companies, hardware manufacturers, and IoT solution providers focusing on real-time data processing
Common Search & Comparison IntentUnderstanding hardware-focused AI deployment and real-time processing capabilities

Edge Ai specialists focus on deploying AI models directly on hardware devices at the edge, emphasizing real-time processing and hardware integration. Data Scientists, however, primarily analyze data, develop models, and work in cloud or server environments. While both roles involve AI and machine learning, Edge Ai is more hardware-centric, whereas Data Scientists focus on data analysis and model development in software environments.

What is edge AI and how does it work?

Edge AI refers to deploying artificial intelligence algorithms directly on devices at the edge of the network, such as sensors or smartphones, rather than relying on centralized cloud servers. It processes data locally, enabling faster response times, reduced bandwidth use, and improved privacy. Professionals working with edge AI often need skills in embedded systems, machine learning, and hardware integration.
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Edge Ai jobs near you
Infographic showing various Edge Ai job openings in the United States as of August 2026, with employment types broken down into 82% Full Time, 12% Part Time, and 6% Contract. Highlights an 76% In-person, 12% Hybrid, and 12% Remote job distribution, with an average salary of $42,216 per year, or $20.3 per hour.

Edge AI/Model Optimization Engineer

NextGen Federal Systems

Aberdeen, MD

$123K - $147K/yr

Full-time

Re-posted 13 hours ago

Job description

NextGen is seeking a highly motivated and technically skilled Edge AI/Model Optimization Engineer to support the deployment, optimization, and sustainment of AI and agentic AI capabilities within edge and tactical computing environments. This role focuses on evaluating, tuning, benchmarking, and operationalizing Large Language Models (LLMs), embedding models, and AI inference services for constrained hardware platforms, including the X9 Spider Mission Computer architecture and other edge compute systems supporting operational missions using ReadiChat.

ReadiChat is a mission-focused, agentic AI platform designed to help organizations build, deploy, govern, and scale specialized AI agents for operational workflows. It combines AI agents, workflow orchestration, grounded knowledge, testing frameworks, and enterprise controls into a single collaborative workspace.

The ideal candidate will possess expertise in AI model optimization, GPU-enabled edge computing, runtime performance tuning, and operational AI deployment. This role requires close collaboration with AI engineers, systems integrators, mission stakeholders, and operational users to ensure AI-enabled capabilities remain performant, reliable, and mission-effective within disconnected, degraded, intermittent, and low-bandwidth environments.

Responsibilities
  • Evaluate candidate Large Language Models (LLMs), embedding models, and AI inference solutions for quality, latency, memory utilization, reliability, and operational performance on embedded GPU-enabled edge compute platforms, including the X9 Spider Mission Computer architecture.
  • Tune and optimize AI model runtime configurations for edge deployment, including quantization strategies, batching configurations, context window sizing, cache behavior, inference scheduling, and GPU memory utilization specific to operational edge hardware environments.
  • Collaborate with customer stakeholders to assess mission requirements and evaluate alternative edge compute platforms when operational demands exceed X9 Spider capabilities or when cost, performance, power, size, weight, or thermal tradeoffs require additional analysis.
  • Benchmark agentic AI workflows, inference pipelines, and model-serving architectures against target hardware constraints and operational performance thresholds.
  • Recommend model-selection, runtime, and configuration tradeoffs balancing mission effectiveness, latency, throughput, resource utilization, reliability, and operational sustainability.
  • Build and maintain repeatable performance and stress-testing frameworks for evaluating latency, throughput, tool-call overhead, failover behavior, degraded-resource conditions, and disconnected operational scenarios on edge compute platforms.
  • Package, deploy, validate, and sustain local model-serving components and inference services to support reliable operation within tactical and edge environments.
  • Collaborate with agent engineers, AI developers, and integration teams to validate that agent behavior, workflow reliability, and operational outcomes remain acceptable following model compression, quantization, runtime optimization, or hardware configuration changes.
  • Support deployment, troubleshooting, optimization, and sustainment activities for AI-enabled applications operating in edge, airborne, tactical, or disconnected operational environments.
  • Train customer technical personnel on supported model profiles, operational constraints, runtime tuning considerations, deployment limitations, troubleshooting procedures, and platform sustainment best practices.
  • Maintain technical documentation, benchmarking results, model validation reports, deployment procedures, optimization baselines, configuration guides, and operational support materials.
  • Support DevSecOps and CI/CD activities associated with AI model packaging, deployment automation, runtime validation, and operational release processes.
Required Qualifications
  • Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, Data Science, Artificial Intelligence, or related technical discipline.
  • 5+ years of experience supporting AI/ML deployment, model optimization, edge computing, GPU acceleration, or AI inference operations.
  • Experience deploying and optimizing LLMs, embedding models, or AI inference pipelines within resource-constrained or edge-compute environments.
  • Experience with GPU-enabled systems and inference optimization technologies such as CUDA, TensorRT, ONNX Runtime, vLLM, Ollama, or equivalent platforms.
  • Experience tuning AI runtime configurations including quantization, batching, caching, and memory optimization techniques.
  • Experience benchmarking AI models and operational workflows against hardware performance constraints.
  • Experience with Linux-based systems, containerized deployments, and orchestration technologies such as Docker and Kubernetes.
  • Familiarity with Python and AI/ML deployment frameworks commonly used for edge inference and operational AI systems.
  • Strong analytical, troubleshooting, and performance optimization skills.
  • Ability to communicate technical findings and operational tradeoffs effectively to technical and non-technical stakeholders.
  • Active Security Clearance is required
Desired Qualifications
  • Experience supporting tactical, airborne, or mission-command edge computing environments.
  • Familiarity with X9 Spider Mission Computer architectures or similar embedded GPU-enabled mission systems.
  • Experience supporting AI-enabled workflows within NGC2, AIDP, EMSCO, Lattice, or related operational ecosystems.
  • Experience with model quantization techniques such as INT8, FP16, GGUF, GPTQ, AWQ, or similar optimization approaches.
  • Familiarity with disconnected, degraded, intermittent, and low-bandwidth (DDIL) operational environments.
  • Experience with hardware evaluation and performance trade studies for operational edge compute systems.

About NextGen:

NextGen Federal Systems is an innovative technology and professional services provider specializing in advanced software solutions and comprehensive mission and business support services. We work in close collaboration with our customers to truly understand their business and mission goals. Our approach is to design, build, implement, and manage solutions that measurably improve our client's organizational performance. We have established and foster a corporate culture where we:

  • Treat employees with fairness and respect regardless of their position, sexual identity, race, or tenure.
  • Communicate the importance of our mission and our employees' contributions to it, ensuring they understand how their job role contributes to the greater good.
  • Openly promote and communicate our ideas for change and adaptability.
  • Strive to achieve results as an organization.
  • Hold employees accountable to their commitments and provide incentives that encourage positive and productive behaviors.
  • Value the talents and contributions of our employees as the key factor for our success.
  • Create an environment where people can engage at all levels.
  • Encourage people to take risks and allow them to make mistakes.

Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities.

RefID: A01

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