As a Manufacturing Intelligence Engineer, you will analyze manufacturing data to drive corrective actions and improve yield and throughput while collaborating with engineering teams and stakeholders.
Instrumental builds the manufacturing acceleration platform behind the world’s most complex electronics. We capture digital exhaust and engineering context from assembly lines—images, test logs, BOM data, performance, repair cycles—and our AI engines identify insights that are difficult or impossible for human engineers to find. We accelerate the companies building the AI era by improving manufacturing yield, throughput, and ramp. NVIDIA, Meta, L3Harris, and their manufacturing partners rely on Instrumental to accelerate new product introduction and production.
Manufacturing advanced electronics requires understanding millions of signals generated across complex assembly processes. Instrumental builds systems that capture and analyze those signals — images, test results, and process data — enabling engineers to discover failures, identify root causes, and deploy production controls that improve yield and product maturity. Leading companies such as NVIDIA, Cisco, and Meta rely on Instrumental to accelerate new product development and scale manufacturing across global factories. Instrumental has become mission-critical for manufacturers building and scaling the next generation of AI infrastructure hardware.
What We're Looking For:
- You have hands-on experience in electronics or semiconductor manufacturing — whether in failure analysis, test engineering, process engineering, quality, hardware development, or a similar technical role.
- You're comfortable working with messy, real-world manufacturing data to identify patterns, form hypotheses, and determine what evidence is needed before recommending action.
- You know how to investigate root cause across production data, test data, supply chain data, process history, product context, and customer-reported symptoms.
- You don't stop at analysis — you drive corrective actions and can demonstrate measurable impact on yield, failure analysis cycle time, throughput, scrap/rework, or bonepile recovery.
- You communicate complex technical findings clearly to diverse audiences: engineering teams, OEM/CSP stakeholders, manufacturing operators, and internal teammates.
- You're excited to work with next-generation AI tools and apply rigorous engineering judgment to AI-generated findings.
- You're product-minded — you can spot workflow friction, identify product gaps, and recognize repeatable customer use cases while staying focused on delivering outcomes.
- You thrive in ambiguity, take ownership without waiting for perfect instructions, and are energized by solving hard technical problems with real customer stakes.
What You’ll Be Doing:
- Own customer investigations using Instrumental Solve, turning ambiguous manufacturing questions into structured analyses, validated findings, and recommended next steps.
- Work directly with customer engineering teams, OEMs, CSPs, and manufacturing stakeholders to investigate issues impacting yield, throughput, failure analysis time, bonepile recovery, scrap/rework, or time-to-market.
- Use manufacturing data across production, test, supply chain, environment, product, logs, and customer-reported issues to identify patterns, form hypotheses, and pressure-test likely root causes.
- Drive corrective actions with customer teams and manufacturing sites, ensuring findings lead to real changes in process, product, supplier, or operational behavior — building credibility and driving sustained Solve adoption along the way.
- Quantify the impact of Solve investigations by connecting actions taken to measurable improvements in customer manufacturing metrics.
- Partner closely with Instrumental Product and Engineering to identify product gaps, repeatable use cases, and workflow improvements that make Solve more valuable across customers.
- Help define how Instrumental delivers Solve outcomes as the product scales, including investigation playbooks, customer workflows, and best practices for closing the loop from insight to impact.
We’re a growing team that works collaboratively, supports each other, and is energized by having impact. We value passion and the ability to learn – you’re encouraged to apply even if your experience doesn’t match the job description precisely!
The following is a representative annual base salary range for this position within the Bay Area: $132,000-$179,000. This position is also eligible for performance-based cash bonuses and growth-stage equity participation. In addition, job level and salary opportunities are evaluated through our interview process – we review the experience, knowledge, skills, and abilities of each applicant.
Instrumental is proud to offer a highly-rated variety of benefits, including health, vision, dental, commuter plans, and parental leave.
At Instrumental, protecting company and customer information is a shared responsibility. All employees are expected to comply with company security and privacy policies and promptly report suspected security incidents or policy violations.
Instrumental Palo Alto, California, USA Office
909 Alma Street, Palo Alto, CA, United States, 94301
Similar Jobs
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
Lead design and delivery of SMAC workflow methodology and infrastructure to keep system specifications and manufacturing test specs aligned. Build production-grade Python pipelines, automated checks, CI gates, and agent-ready tooling that detect spec drift pre-silicon. Integrate workflows into program milestones, drive cross-org adoption across design, operations, and DFX, and apply AI where appropriate while ensuring reviewable, auditable artifacts.
Top Skills:
AteBltCiCliData PipelinesL10+LlmProduction ServicesPythonSlt
Hardware • Security
Design and maintain SQL-based data pipelines and Power BI semantic models to transform ERP and manufacturing data into actionable dashboards and automated analytics. Partner with stakeholders to improve reporting accuracy, resolve data quality issues, support ERP modernization, and apply automation and AI tools to enhance operational decision-making.
Top Skills:
Ai-Enabled ToolsAzure SqlDaxErpInfor XaMicrosoft FabricPower AutomatePower BIPower Bi ServicePower QuerySemantic ModelsSQLStored Procedures
Artificial Intelligence • Hardware • Software
Develop, deploy, and operate agentic AI systems that drive manufacturing decisions across S&OP, material planning, production, quality, logistics, and customer deployments. Embed with cross-functional teams to model demand, supply, BOMs, capacity, and risk; build agents that generate executable plans, propagate design changes, run historical replays and live evals, and continuously evaluate frontier models and optimization methods to improve ramp speed and schedule attainment.
Top Skills:
Agentic Coding ToolsAgentic SystemsBomConstraint OptimizationDigital TwinsEcoErpForecastingLlm AgentsLlmsMesMrpOrchestrationPlmPythonQmsSimulationTransformers
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
