Principal Software Engineer — AI, Data & MEMS

 About Us 

Menlo Micro is redefining electronic switching with its proprietary Ideal Switch® technology, a new class of switch that overcomes the inherent trade-offs of traditional electromechanical relays (EMRs) and semiconductor switches. Spanning both high-frequency and power applications, the Ideal Switch product portfolio combines ultra-low loss, high linearity, fast switching and compact form factors to enable new levels of performance, density and scalability. 

Adopted across some of the world’s most demanding applications, from advanced semiconductor test for memory and high-performance computing (HPC) to AI infrastructure, data centers, aerospace and defense, Menlo Micro helps customers solve critical switching challenges and build smaller, lighter and more efficient systems. The Ideal Switch enables improved signal integrity, reduced size, weight and power consumption (SWaP), and simplified architectures, helping improve system economics, accelerate innovation and bring differentiated products and capabilities to market. 

For more information, visit www.menlomicro.com or follow Menlo Micro on LinkedIn. 

About the Role 

As a Principal Software Engineer - AI, you will shape the architecture and technical direction of Menlo Micro's software, data and AI platforms across the full semiconductor lifecycle: MEMS device design, wafer fabrication, test and qualification, product documentation, and customer-facing engineering tools. You will combine deep software engineering expertise with hands-on experience in cloud architecture, AI/ML, multi-agent systems, computer vision and physics-informed modeling to turn complex device, process and manufacturing challenges into reliable, scalable production systems. 

This role spans the full development lifecycle, from research and proof of concept through platform development, model deployment, fab and test-floor integration, and production operations. You will architect the data, modeling and compute foundations that enable smarter device design, higher-yield manufacturing, faster qualification, and AI-assisted engineering and customer support. 

As a senior individual contributor, you will lead technical initiatives across teams, establish engineering standards, mentor engineers, and partner with Device Design, Process and Fab Engineering, Test & Reliability, Applications Engineering, Operations, IT and business stakeholders to deliver measurable engineering, manufacturing and customer outcomes. 

What You'll Be Doing 

• Own architecture and technical direction. Define software and platform architectures across cloud, edge, data and AI systems serving design, fab, test and customer-facing functions. Guide decisions that balance performance, reliability, security, IP protection, cost and speed of delivery. 

• Build scalable software and data platforms. Design distributed services, reusable integration frameworks, and batch and streaming pipelines that unify design data, wafer-level and final test results, fab process data, reliability data, PLM/ERP records and customer telemetry for analytics, model training and real-time inference. 

• Lead AI/ML development and deployment. Architect systems supporting model development, training, validation, deployment, monitoring and continuous improvement for yield prediction, defect classification, test optimization and device characterization. Establish reproducible workflows for datasets, experiments, model versions and production releases. 

• Architect multi-agent AI platforms. Build platforms where specialized agents collaborate across device design, process engineering, test engineering, applications support and operations. Define orchestration, task delegation, shared context and memory, agent communication, and secure tool integration with engineering and enterprise systems. 

• Engineer reliable agent execution. Implement durable workflows, controlled parallelism, state management, retries, failure recovery and human review. Establish tracing and evaluation to measure task completion, output quality, latency and cost across individual agents and coordinated workflows. 

• Advance AI for manufacturing and test. Architect real-time inference pipelines and decision frameworks for wafer inspection, process control, adaptive test and yield management. Partner with fab, test and reliability engineers to integrate learned models with deterministic process controls, spec limits and fallback behavior. 

• Develop computer vision and engineering representations. Build systems that interpret wafer maps, optical and SEM inspection images, and probe and package test data, and that represent structured engineering artifacts, including netlists, layouts, schematics, S-parameter data and physical device models, to support analysis, reasoning and automation. 

• Enable physics-informed learning and optimization. Develop systems that explore MEMS device, material and packaging design spaces and evaluate candidate designs against electromechanical, thermal and RF constraints, simulation results and measured data. 

• Accelerate simulation and engineering iteration. Build learned surrogate models that approximate computationally expensive FEA, multiphysics and electromagnetic simulations. Establish validation methods that quantify accuracy, uncertainty and appropriate operating boundaries. 

• Automate expert engineering workflows. Integrate AI agents with device knowledge, datasheets and application notes, EDA and simulation environments, test systems and enterprise tools to assist and progressively automate design review, qualification reporting, failure analysis and customer applications support, with appropriate permissions, traceability and oversight. 

• Establish production engineering standards. Drive automated testing, observability, data quality, model evaluation, security and IP controls, incident response, and high-availability and disaster-recovery practices across software and AI platforms, including ITAR/export-control-aware handling of sensitive data. 

• Lead across teams. Resolve complex technical problems, guide architecture reviews, mentor engineers, and communicate technical decisions clearly to engineering teams, executives and non-technical stakeholders. 

What You'll Bring 

• A master's or doctoral degree in Computer Science, Software Engineering, Electrical Engineering, Applied Physics, Applied Mathematics or a related technical discipline. 

• Extensive software engineering experience, including ownership of complex production platforms and technical leadership across multiple teams or engineering domains. 

• A minimum of 5 years of relevant AI/ML experience with a bachelor's degree, 3 years with a master's degree, or 1 year with a Ph.D., covering model development, training, validation and deployment. 

• Demonstrated ability to operate at Principal Engineer scope: setting technical direction, making architectural decisions across teams, influencing stakeholders, and delivering sustained improvements in engineering quality and business outcomes. 

• Strong programming expertise in Python and one or more additional languages, such as C++, Java, Go, TypeScript or Scala, with sound knowledge of software design, algorithms, concurrency and distributed systems. 

• Deep experience designing cloud-native platforms on AWS or an equivalent cloud provider, including APIs, event-driven services, identity and federation, data storage, infrastructure as code and CI/CD. 

• Strong experience with SQL and NoSQL systems, data modeling, batch and streaming processing, and modern data platforms such as Snowflake, BigQuery or Redshift, including time-series and high-volume test and sensor data. 

• Hands-on experience with modern ML frameworks and production inference systems, including model evaluation, latency optimization, deployment strategies, and monitoring for quality and drift. 

• Experience building and operating multi-agent AI platforms, including orchestration, state management, agent communication, retrieval-augmented generation over technical documentation, and secure tool integration. Ability to select appropriate single-agent, multi-agent and deterministic workflow patterns based on complexity, reliability and cost. 

• Experience applying AI/ML to computer vision, semiconductor or electronics manufacturing, physical systems, engineering optimization or simulation, with an understanding of how models are validated against measured, real-world behavior. 

• Strong understanding of production reliability, observability, access controls and operational support for software, data and AI systems. 

• Strong communication and mentoring skills, with the ability to simplify complex concepts and build alignment across software, data, device, process, test, product and business teams. 

Preferred Qualifications 

• Experience with graph-based learning, multimodal models, physics-informed methods, surrogate modeling, or simulation-driven optimization, particularly for multiphysics (electromechanical, thermal, RF/electromagnetic) problems. 

• Familiarity with RF and power electronics concepts such as S-parameters, insertion loss, linearity and power handling, and with EDA, FEA and electromagnetic simulation tools. 

• Experience with agent orchestration frameworks and managed runtimes, including agent interoperability and tool integration through protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A), and building AI assistants over versioned technical documentation such as datasheets and application notes. 

• Experience establishing evaluation datasets, automated benchmarks and regression testing for AI models and agent workflows. 

• Experience with real-time or resource-constrained edge inference, sensor fusion, control systems, or industrial automation and factory systems. 

• Experience supporting platform migrations, enterprise system integration (PLM, ERP, CRM) or scaling software for a growing manufacturing footprint. 

• Familiarity with AWS services for serverless computing, streaming, data engineering, model training and generative AI (for example, Bedrock and Bedrock AgentCore, SageMaker, Kinesis, Glue). 

• Familiarity with export-control (ITAR/EAR) and IP-protection requirements for data and AI systems in a defense-serving semiconductor company. 

The position is remote and hybrid.  The candidate will need to travel frequently to Albany NY, Nashville TN, and Irvine CA offices.  Candidate should be willing to travel to all customers across the United States as needed.