Principal Software Engineer — AI, Data & MEMS

Menlo Micro•*, CA, CA

3d•$170,000 - $171,000•Hybrid

About The Position

Menlo Micro is seeking a Principal Software Engineer to shape the architecture and technical direction of its software, data, and AI platforms across the semiconductor lifecycle, including MEMS device design, wafer fabrication, test and qualification, product documentation, and customer-facing engineering tools. The role combines software engineering with cloud architecture, AI/ML, multi-agent systems, computer vision, and physics-informed modeling to address device, process, and manufacturing challenges with reliable, scalable production systems. The position spans research and proof of concept through platform development, model deployment, fab and test-floor integration, and production operations. The engineer will build data, modeling, and compute foundations to support device design, manufacturing yield, qualification, engineering, and customer support. As a senior individual contributor, this person will lead technical initiatives across teams, establish engineering standards, mentor engineers, and partner with engineering, operations, IT, and business stakeholders. The position is remote and hybrid, with frequent travel to Menlo Micro offices in Albany, New York; Nashville, Tennessee; and Irvine, California, as well as travel to U.S. customers as needed.

Requirements

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.
At least 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 Principal Engineer-level scope, including setting technical direction, making cross-team architectural decisions, influencing stakeholders, and delivering sustained improvements in engineering quality and business outcomes.
Strong Python programming expertise and proficiency in one or more additional languages such as C++, Java, Go, TypeScript, or Scala; 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 quality and drift monitoring.
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 choose single-agent, multi-agent, or 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 understanding of validation 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, including the ability to explain complex concepts and build alignment across software, data, device, process, test, product, and business teams.
Willingness to travel frequently to Albany, Nashville, and Irvine offices and to customers across the United States as needed.

Nice To Haves

Experience with graph-based learning, multimodal models, physics-informed methods, surrogate modeling, or simulation-driven optimization, particularly for electromechanical, thermal, or RF/electromagnetic multiphysics 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, agent interoperability, and tool integration through protocols such as MCP and A2A; experience building AI assistants over versioned technical documentation.
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, industrial automation, or factory systems.
Experience supporting platform migrations, enterprise system integration (PLM, ERP, CRM), or software scaling for a growing manufacturing footprint.
Familiarity with AWS services for serverless computing, streaming, data engineering, model training, and generative AI, such as Bedrock, Bedrock AgentCore, SageMaker, Kinesis, and Glue.
Familiarity with ITAR/EAR export-control and IP-protection requirements for data and AI systems in a defense-serving semiconductor company.

Responsibilities

Define software and platform architectures across cloud, edge, data, and AI systems supporting design, fabrication, testing, and customer-facing functions.
Design distributed services, reusable integration frameworks, and batch and streaming pipelines that unify design, test, fab, reliability, PLM/ERP, and customer telemetry data.
Architect AI/ML systems and reproducible workflows for model development, training, validation, deployment, monitoring, and continuous improvement.
Build multi-agent AI platforms, including orchestration, task delegation, shared context and memory, agent communication, and secure integration with engineering and enterprise tools.
Implement reliable agent execution with durable workflows, state management, retries, failure recovery, human review, tracing, and evaluation.
Develop real-time inference pipelines and decision frameworks for wafer inspection, process control, adaptive testing, and yield management, in partnership with fab, test, and reliability engineers.
Build systems to interpret wafer maps, optical and SEM inspection images, probe and package test data, and structured engineering artifacts such as netlists, layouts, schematics, S-parameter data, and physical device models.
Develop systems for physics-informed learning and optimization of MEMS device, material, and packaging designs against engineering constraints, simulations, and measured data.
Build and validate surrogate models for computationally expensive FEA, multiphysics, and electromagnetic simulations, including methods to quantify accuracy, uncertainty, and operating boundaries.
Integrate AI agents with device knowledge, technical documentation, EDA and simulation environments, test systems, and enterprise tools to assist with engineering and customer-support workflows, with appropriate permissions and oversight.
Establish production standards for automated testing, observability, data quality, model evaluation, security, IP controls, incident response, high availability, disaster recovery, and sensitive-data handling.
Lead technical problem-solving and architecture reviews, mentor engineers, and communicate decisions to technical and non-technical stakeholders.