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Requirements:
Minimum Qualifications
5+ years of experience in SoC or IP verification
Advanced knowledge of SoC architecture/design, in-depth knowledge of verification flows and broad system view
Expected to have a deep understanding and shown experience in advanced verification processes, including coverage driven and formal methods
Extensive experience with SystemVerilog and UVM
Experience with verification infrastructure development
Knowledge of storage IPs and control oriented design - an advantage
Knowledge of formal, hardware acceleration - an advantage
Scripting and programming experience using several of the following: Perl, Python, Verilog, SystemVerilog, C, C++, and TCL
Preferred Qualifications
B.Sc / M.Sc in Electrical or Computer Engineering

Requirements:
What You Need for the Role
6+ years building and shipping production backend systems, including experience leading technical initiatives, processes and teams.
5+ years experience managing engineers, hiring, running 1:1s, giving feedback, and developing people, not just tech-leading.
Strong server-side engineering in at least two of: Node.js / JavaScript, Python, Java. Able to own a feature from data model to API.
Solid PostgreSQL - schema design, query writing and optimization, working with relational data at scale.
Proven experience designing and operating APIs (HTTP/REST/GraphQL) and a working command of auth and access control: OAuth 2.0, API keys, scopes, and RBAC.
Hands-on experience building LLM-powered features in production - direct work with LLM provider APIs (Anthropic, OpenAI, Gemini, Ollama or similar), function/tool calling, token optimization, structured outputs, and prompt engineering.
Experience with agentic systems: designing tool interfaces an LLM can use reliably, multi-step agent loops, and handling failure and recovery.
Working knowledge of MCP (Model Context Protocol) - or the depth in tool-calling and AI integration to ramp on it quickly - and a clear sense of what makes a tool definition easy for a model to use well.
Experience evaluating LLM systems: building eval suites, measuring tool-call and decision accuracy, and instrumenting AI behavior with tracing and observability.
A security mindset -treat prompt injection, untrusted inputs, least-privilege design, and data exposure as first-order concerns, not afterthoughts.
Comfortable in a small, fast-moving startup - high ownership, low hand-holding, and a willingness to build processes and infrastructure from scratch when needed.


Advantages
Retrieval and context engineering - RAG, embeddings, vector search, and semantic retrieval used to ground agents in API docs, schemas, and internal knowledge.
Experience running an MCP server, or a comparable AI-tooling integration, in production.
Cloud infrastructure (AWS / GCP / Azure), containers, and IaC.
Cybersecurity domain knowledge.
Open-source contributions to AI / agent tooling.

Requirements:
Minimum Qualifications
Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering in a leading University
4+ years of experience in software or test automation engineering
Proficiency with Python and Pytest
Experience testing embedded systems, firmware, or hardware-in-the-loop (HIL) setups
Experience building and maintaining CI/CD pipelines
Working knowledge of Git and Linux environments
Ability to quickly ramp up in new technical domains and adopt unfamiliar technologies
Preferred Qualifications
Experience building end-to-end test infrastructure for hardware/software automation projects
Strong failure analysis, troubleshooting, and system-level debugging skills
Experience with lab instruments (power supplies, oscilloscopes, etc.) and their automation APIs
Experience with MCUs (such as ESP32 or STM32) for building test fixtures and station controllers
Strong Bash scripting skills
Strong written communication skills, with the ability to turn a complex failure into a clear, reproducible bug report