Embedded AI Engineer

Location: 100% remote within the United States

Salary: $100,000–$150,000 annually

Company

hirify.global is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

What you will do

• Design and implement edge AI solutions for mobile SoCs, NPUs, DSPs, GPUs, and embedded accelerators.
• Apply quantization, pruning, distillation, and architectural optimization to meet compute, memory, energy, and connectivity constraints.
• Build cross-platform inference runtimes using TensorFlow Lite, ONNX Runtime, Core ML, and related technologies.
• Develop on-device model update, versioning, rollback, hybrid edge-cloud, and privacy-conscious telemetry workflows.
• Collaborate with hardware, firmware, and product teams while implementing secure execution, model protection, and integrity verification.
• Create benchmarking suites and technical documentation covering accuracy, latency, energy trade-offs, architecture, configuration, and operations.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
• Six or more years of ML engineering experience, including significant edge or mobile AI work.
• Strong proficiency in Python and C++.
• Hands-on experience with model compression, quantization, pruning, production ML deployment, and performance profiling.
• Experience with at least one major edge inference framework and a solid understanding of mobile and embedded hardware architectures.
• Applicants must be U.S. citizens, Green Card holders, EAD holders, or candidates eligible for H-1B transfer; new H-1B sponsorship is not available.

Nice to have

• Experience with custom NPU or DSP toolchains.
• Familiarity with federated learning or on-device personalization.
• Experience with safety-critical or industrial edge deployments.
• Open-source contributions to edge AI frameworks.
• Experience optimizing LLMs for on-device inference.

Culture & Benefits

• Full-time direct W-2 employment.
• Opportunity to work on production AI systems for resource-constrained edge devices.
• Cross-functional collaboration with hardware, firmware, and product specialists.
• Emphasis on responsible AI, on-device privacy, security, and bias evaluation.
• Established organization with career growth opportunities.