Application Engineer

Efabless.Com - Palo Alto, CA

Full-time

Remote - Palo Alto, CA

Computer and Electronic Product Manufacturing

About the position

The Applications Engineer at Efabless will be responsible for designing and developing reference designs and embedded systems specifically for machine learning applications at the edge. This role involves integrating machine learning algorithms into embedded platforms, collaborating with cross-functional teams, and providing technical support to customers. The position offers an exciting opportunity to work on innovative projects in a dynamic and collaborative environment.

Responsibilities

• Develop reference designs and embedded systems for machine learning applications at the edge, focusing on performance, low power consumption, and reliability.
• Integrate machine learning algorithms into embedded platforms, ensuring seamless communication between hardware and software components.
• Build and test prototypes for ML-driven edge devices, including developing hardware-software co-designs and optimizing system performance.
• Work closely with cross-functional teams, including hardware design engineers, data scientists, and firmware developers, to ensure successful project execution.
• Provide technical support to customers, helping them integrate and deploy our reference designs into their solutions, and troubleshoot any issues that arise.
• Create comprehensive documentation, including application notes, reference manuals, and white papers, to support the use and integration of reference designs.
• Stay up to date on the latest trends and technologies in embedded systems and machine learning, evaluating new tools, platforms, and frameworks that can enhance our offerings.

Requirements

• Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, or a related field.
• 5+ years of experience in embedded systems development, with a focus on edge computing and machine learning applications.
• Strong understanding of embedded systems architecture, including microcontrollers, FPGAs, and SoCs.
• Experience with real-time operating systems (RTOS) and low-power system design.
• Proficiency in programming languages such as C, C++, Python, and knowledge of embedded Linux.
• Experience with ML frameworks like TensorFlow Lite, PyTorch, and ONNX, particularly for edge devices.
• Knowledge of communication protocols (e.g., I2C, SPI, UART, and LoRa).
• Ability to troubleshoot complex technical issues and provide creative, efficient solutions.
• Strong written and verbal communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

Nice-to-haves

• Experience with hardware accelerators for machine learning (e.g., GPUs, TPUs, NPUs).
• Familiarity with edge AI platforms such as NVIDIA Jetson, Google Edge TPU, or similar.
• Hands-on experience in the deployment and optimization of machine learning models for constrained devices.
• Familiarity with sensor interfaces and IoT communication protocols (e.g., MQTT, BLE, NB-IoT).
• Prior experience in customer-facing technical roles, such as technical support or pre-sales engineering.

Benefits

• Competitive salaries
• Benefits
• Opportunities for professional growth