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Member of Technical Staff (MTS)

SketchPro.ai

San Francisco, CAFull-time$180–250K/yrPosted todayStill listed today

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At a glance

Compensation
$180–250K/yr
Location
San Francisco, CA
Schedule
Full-time
Work Authorization
Not specified

Olive lists jobs from US employers, including remote roles you can work from the United States.

Job overview

SketchPro is seeking a Member of Technical Staff to apply LLMs and VLMs to agentic workflows for 3D CAD and construction documentation. The role combines ML research with real-world architecture, engineering, and construction workflows, including integrations with industry platforms. The position is fully in-person five days per week at the San Francisco office.

Skills & qualifications

RequiredNice to have

Skills

Machine LearningArtificial IntelligenceComputer-Aided DesignTechnical DocumentationNew Product DevelopmentOther EngineeringAI Product DevelopmentProblem SolvingResourcefulnessSelf-DirectionLLMsVLMsAgentic Workflows3D CADAutodesk RevitBluebeamProcoreGeometry GenerationDraftingConstruction Documentation

Qualifications

Startup Experience

Benefits

Medical Insurance
Dental Insurance
Vision Insurance

Full job description

About SketchPro SketchPro is building the first AI architect: an AI copilot for architects and engineers that accelerates construction documentation. We work with leading architecture firms to help streamline production and deliver documentation at the highest level. The Role We’re looking for a Member of Technical Staff to apply SOTA LLMs and VLMs to build agentic workflows for 3D CAD and documentation. This role will sit at the intersection of cutting-edge ML research and real-world AEC workflows, applying frontier LLMs to real customer problems. This role is fully in-person 5x/week at our SF office next to Salesforce Tower. What You’ll Do

  • Build agentic architecture using LLMs and VLMs to reason over 2D + 3D data

  • Build real-world integrations into industry platforms like Autodesk Revit, Bluebeam, Procore, and more

  • Collaborate with architects, engineers, and product teams to deeply understand CAD workflows and translate them into AI-first systems.

  • Prototype and iterate rapidly on LLMs for geometry generation, drafting, and construction documentation.

Who You Are

  • Strong product/engineering background building products with AI at their core

  • Demonstrated ability to take ambiguous problems and craft reliable and efficient solutions.

  • Thrives in early-stage environments: self-directed, resourceful, excited to build 0 → 1. Previous startup experience preferred.

Why Join Us

  • Shape the future of how buildings get designed and built.

  • Work on frontier AI problems applied to a $100B market that is overlooked by Silicon Valley

  • Join a fast-growing, venture-backed team supported by Khosla Ventures, Neo, and top leaders from OpenAI, Ramp, Harvey, Brex, and more.

  • Competitive compensation ($180k–$250k salary + competitive equity)

Benefits

  • Medical, Dental, Vision coverage

  • Expensed lunch and dinner 5x/week in SF office

  • Health + Wellness Stipend

How this job compares

Set against the 6,278 open Machine Learning Engineer jobs on Olive Jobs, counted November 9, 2026.

Pay
  • This posting$180–250K/yr
  • California median$180–263K/yr1,462 salary ranges
  • US median$150–246K/yr5,126 salary ranges

This range's midpoint, $215K, is 4% below the California median midpoint ($225K) — in the middle half of Machine Learning Engineer salary ranges in California.

Openings
Olive Jobs lists 1,939 open Machine Learning Engineer jobs in California and 6,278 across the US. 8% of them nationwide are listed as remote or flexible.
Hiring the most in California
Experience
Machine Learning Engineer postings that state a minimum ask for 5 years of experience at the median; the middle half ask for 3 to 7 years (4,993 postings).
Education
2,037 of the 6,278 open Machine Learning Engineer jobs (32%) ask for a bachelor's degree or higher.

Counted across the open, public US postings on Olive Jobs on November 9, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.

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