{"schemaVersion":"jobsearcher.job.v1","id":"7c226589674f730597a46338","url":"https://jobsearcher.com/jobs/7c226589674f730597a46338","canonicalUrl":"https://jobsearcher.com/jobs/7c226589674f730597a46338","title":"Forward Deployed Engineer","description":"About Normal Computing\nNormal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic, in-memory, asynchronous: the result is 10-100× more AI inference per dollar, per watt.\nWe co-design the full stack: AI-native EDA systems in production with the world's largest semiconductor companies, and the advanced ASICs they make possible. Backed by $85M+ from the world's leading deep-tech investors and built by scientists, engineers, and operators from the labs that built modern computing.\nNormal works as one team across New York, Silicon Valley, London, Copenhagen, and Seoul. We hire people who want the hardest version of their craft, across every discipline, at every seniority.\nThe Role\nThe cost of taping out silicon is enormous, and the complexity of verification makes multiple tapeouts hard to avoid. Normal EDA accelerates this work as an AI platform for collaborative silicon engineering: a single source of truth across the silicon engineering lifecycle, learning continuously from the teams that use it. As a Forward Deployed Engineer, you own our EDA system inside a customer's environment. Embedded directly with our partners, you adapt our platform to their data, workflows, and design challenges, working alongside our account executive and a deployment strategist to make the deployment a success.\nYou thrive as a problem-solver and take pride in winning over customers along with the rest of your team. You will be debugging distributed systems, post-training models, and working in SystemVerilog, because that is the language of our customers.\nWhat You'll Own\nProduction Problem-Solving: Diagnose issues in our system, the model, the data, or the workflow. Work deep in both Normal's systems and the customer's environment to resolve them, and close the loop with their engineers.\nEvaluation Against Reality: Design and run evals against real customer workflows, validating generated artifacts against their specifications so model behavior holds up in production.\nPlatform Integration: Integrate the platform with each customer's data, design flows, and tooling, working with their production codebases and against their existing infrastructure.\nCustomer Signal: Embed with silicon design teams, translate their constraints into model and platform requirements, and carry that signal back to Normal's research, product, and platform teams to shape what gets built next.\nContinual Learning: Post-train Normal's models on each customer's proprietary data and trajectories, and build the continual-learning loops that turn their engineers' feedback into system knowledge, so model quality compounds across the engagement.\nJudgment Ahead of Playbook: Make the calls on what to build, what to skip, and when to push back on a request that would compromise what ships. Codify what works into patterns that raise the floor for every engagement after yours.\nWhat Makes You a Great Fit\nGreat at problem-solving and tracking down issues wherever they are in the stack\nStrong software engineering fundamentals: proficient in Python, comfortable in production codebases, distributed-systems literate\nHands-on experience with the modern ML stack: prompt engineering, fine-tuning, evals, RAG, agentic patterns, model deployment\nWillingness and ability to go deep on semiconductor verification workflows. You will spend significant time inside UVM testbenches, SystemVerilog codebases, and design specifications. Prior experience is a strong advantage, but what matters is whether you can build fluency fast and earn credibility with verification engineers\nAn ability to ship ML systems inside customer or production environments where model behavior had to hold up against real-world data\nCalm in ambiguity: you make good decisions with incomplete information, and you know when to act and when to ask\nComfortable with travel when needed\nBonus Points\nDirect experience with EDA, semiconductor design flows, verification workflows (UVM, SystemVerilog, coverage-driven verification), or other formal/structured engineering domains\nBuilt or led an FDE or customer-deployment function from the ground up at an earlier-stage company\nOpen-source contributions or publications in AI or ML venues\nEqual Employment Opportunity Statement\nNormal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.\nAccessibility Accommodations\nNormal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com.\nPrivacy Notice\nBy submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Privacy Policy.\nCompensation Range: $180K - $325K","company":"Normal Computing","rawCompany":"normal computing","city":"Palo Alto","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-03T13:15:39.995Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"17-2061.00","title":"Computer Hardware Engineers","slug":"computer-hardware-engineers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Forward Deployed Engineer","description":"About Normal Computing\nNormal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic, in-memory, asynchronous: the result is 10-100× more AI inference per dollar, per watt.\nWe co-design the full stack: AI-native EDA systems in production with the world's largest semiconductor companies, and the advanced ASICs they make possible. Backed by $85M+ from the world's leading deep-tech investors and built by scientists, engineers, and operators from the labs that built modern computing.\nNormal works as one team across New York, Silicon Valley, London, Copenhagen, and Seoul. We hire people who want the hardest version of their craft, across every discipline, at every seniority.\nThe Role\nThe cost of taping out silicon is enormous, and the complexity of verification makes multiple tapeouts hard to avoid. Normal EDA accelerates this work as an AI platform for collaborative silicon engineering: a single source of truth across the silicon engineering lifecycle, learning continuously from the teams that use it. As a Forward Deployed Engineer, you own our EDA system inside a customer's environment. Embedded directly with our partners, you adapt our platform to their data, workflows, and design challenges, working alongside our account executive and a deployment strategist to make the deployment a success.\nYou thrive as a problem-solver and take pride in winning over customers along with the rest of your team. You will be debugging distributed systems, post-training models, and working in SystemVerilog, because that is the language of our customers.\nWhat You'll Own\nProduction Problem-Solving: Diagnose issues in our system, the model, the data, or the workflow. Work deep in both Normal's systems and the customer's environment to resolve them, and close the loop with their engineers.\nEvaluation Against Reality: Design and run evals against real customer workflows, validating generated artifacts against their specifications so model behavior holds up in production.\nPlatform Integration: Integrate the platform with each customer's data, design flows, and tooling, working with their production codebases and against their existing infrastructure.\nCustomer Signal: Embed with silicon design teams, translate their constraints into model and platform requirements, and carry that signal back to Normal's research, product, and platform teams to shape what gets built next.\nContinual Learning: Post-train Normal's models on each customer's proprietary data and trajectories, and build the continual-learning loops that turn their engineers' feedback into system knowledge, so model quality compounds across the engagement.\nJudgment Ahead of Playbook: Make the calls on what to build, what to skip, and when to push back on a request that would compromise what ships. Codify what works into patterns that raise the floor for every engagement after yours.\nWhat Makes You a Great Fit\nGreat at problem-solving and tracking down issues wherever they are in the stack\nStrong software engineering fundamentals: proficient in Python, comfortable in production codebases, distributed-systems literate\nHands-on experience with the modern ML stack: prompt engineering, fine-tuning, evals, RAG, agentic patterns, model deployment\nWillingness and ability to go deep on semiconductor verification workflows. You will spend significant time inside UVM testbenches, SystemVerilog codebases, and design specifications. Prior experience is a strong advantage, but what matters is whether you can build fluency fast and earn credibility with verification engineers\nAn ability to ship ML systems inside customer or production environments where model behavior had to hold up against real-world data\nCalm in ambiguity: you make good decisions with incomplete information, and you know when to act and when to ask\nComfortable with travel when needed\nBonus Points\nDirect experience with EDA, semiconductor design flows, verification workflows (UVM, SystemVerilog, coverage-driven verification), or other formal/structured engineering domains\nBuilt or led an FDE or customer-deployment function from the ground up at an earlier-stage company\nOpen-source contributions or publications in AI or ML venues\nEqual Employment Opportunity Statement\nNormal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.\nAccessibility Accommodations\nNormal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com.\nPrivacy Notice\nBy submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Privacy Policy.\nCompensation Range: $180K - $325K","datePosted":"2026-08-03T13:15:39.995Z","dateModified":"2026-08-03T13:15:39.995Z","hiringOrganization":{"@type":"Organization","name":"Normal Computing","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Palo Alto","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"7c226589674f730597a46338"},"url":"https://jobsearcher.com/jobs/7c226589674f730597a46338"}}