Apiphany
Lead Deployment Strategist
San Francisco · Senior
Sponsorship not specified$120k-$180kDetected 270 days ago
PythonDatabricksData AnalysisData EngineeringSupply ChainRoboticsLeadershipCommunicationProblem Solving
About the role
- Deep curiosity about engineering, data, and AI-driven decision-making
- 3-5+ years in client-facing project leadership, delivering measurable impact
Responsibilities
- Our models understand physics principles, design specifications, and program constraints.
- We're passionate about transforming hard-tech and building a category-defining company together.
- You'll collaborate with engineers, product designers, and executives-both internally and within customer organizations-to identify high-value problems, map workflows, and deploy Apiphany's intelligence platform in the real world.
Requirements
- 2-7 years of experience in Mechanical, Aerospace, Industrial, Electrical, Manufacturing, Supply Chain, Automotive, or Robotics/Automation
- Familiarity with manufacturing systems, digital twins, or data pipelines
Skills
- Strong analytical, structured problem-solving, and communication skills
Compensation
- $120,000-$180,000
- Visa Sponsorship
- Hybrid work: 3 days in San Francisco office
- Snacks at the office
Benefits
- Medical, Dental, and Vision insurance coverage
- Flexible vacation policy
- Equity: Generous Equity
Company info
- Backed by world-class investors from Markforged, Databricks, GM, and Character, our mission is to revolutionize how engineering decisions are made, turning complexity into clarity for the world's top manufacturers.
- We're a small, elite team of builders from Stanford, Berkeley, MIT, UW, and CMU, alongside industry leaders from GM, Ford, and Genesis Therapeutics.
- This is a role for analytical, product-minded problem solvers who thrive in ambiguity, love working directly with customers, and want to build the bridge between AI technology and real-world transformation.
Visa & Work Authorization
- Visa Sponsorship
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This listing is sourced directly from Apiphany's careers page and normalized into a canonical job model.