Principal Embedded Machine Learning Engineer (AI)

TL;DR

Principal Embedded Machine Learning Engineer (AI): Leading the development of ML models, frameworks, and prototyping pipelines for Edge and mixed-signal systems with an accent on model engineering, optimization, and hardware integration. Focus on architecting ML solutions for resource-constrained embedded platforms, including microcontrollers and SoCs, to solve complex industry problems in Voice, Sense, and Control domains.

Location: Must be based in Austin, Texas (Hybrid)

Company

hirify.global is a leading supplier of low-power, high-precision mixed-signal processing solutions for mobile and consumer applications, driving innovation in audio and haptic technology.

What you will do

• Lead rapid prototyping of ML models for edge intelligence across Voice, Sense, and Control domains.
• Build datasets, design model architectures, and optimize for performance, efficiency, and interpretability.
• Co-design ML architectures with silicon and firmware teams to operate efficiently on constrained hardware.
• Scout external IP, academic work, and startups to inform technical strategy.
• Provide technical leadership and mentorship to engineering teams across the organization.
• Define evaluation metrics and benchmarks to ensure prototypes have clear paths to monetization.

Requirements

• Master’s or Ph.D. in Computer Science, Electrical Engineering, or related field with a focus on ML/AI.
• 8+ years of experience developing and deploying ML systems on the Edge and embedded platforms.
• Expertise in CNNs, RNNs, and Transformer-based models with custom architecture design.
• Proficiency in C/C++/Python for integrating ML inference engines into real-time embedded stacks.
• Experience with quantization, pruning, and compiler-level optimizations for embedded deployment.
• Strong systems thinking capability to balance algorithmic, architectural, and physical power constraints.

Nice to have

• Background in early-stage startups or innovation incubators.
• Experience with generative models for voice or reinforcement learning for control systems.
• Familiarity with MLOps frameworks and distributed training pipelines.
• Experience collaborating with academic labs or open-source communities.

Culture & Benefits

• Focus on frontier innovation in Edge AI and semiconductor markets.
• Collaborative environment spanning hardware, firmware, and algorithm teams.
• Exposure to high-impact, real-world product engineering challenges.
• Commitment to diversity, equal opportunity, and professional growth.