Catastrophe Data Engineer presso Pear VC

About Us:

Rising disasters—from earthquakes to wildfires—are destabilizing the property insurance

market, yet carriers often rely on outdated, incomplete data. ResiQuant is changing that.

Founded by Stanford PhDs and backed by a $4M seed round led by LDV Capital, we fuse

structural engineering with advanced, agentic AI to expose critical vulnerabilities that

standard sources miss. Our multi-hazard platform delivers building-level insights so

insurers across the U.S. can underwrite disaster-exposed properties with confidence,

maintain coverage in high-risk regions, and reward resilience where it matters most—

paving the way for a safer, more sustainable future.

About You:

We're seeking an individual who is passionate about the mission of the company to join us

as Catastrophe Engineer with focus on disaster exposure. We prize candidates who share

our company's vision and are ready to help foster an inclusive and collaborative culture.

As a lean seed startup, we need someone with a scrappy, hands-on approach, eager to

evolve alongside our team, and support the company in all stages of growth. The ideal

candidate is excited to apply their knowledge in catastrophe modeling, data science, and

software development, to shape the trajectory of a groundbreaking company.

Qualifications:

• 5+ years of experience with the major catastrophe models used by insurance

companies (RMS and Verisk) for hurricane, earthquake, severe convective storm,

and wildfire modeling. OR PhD in relevant field.

• Technical understanding about why buildings survive or fail during hurricanes and

wildfires.

• Understanding of statistical concepts and practical experience applying them (in

A|B testing, causal inference, ML, etc.).

• Experience in programming/modeling in Python.

• Knowledge of database systems.

• Background in structural engineering and/or risk analysis.

What will make you stand out:

• Proficiency/Experience in software development.

• Proficiency/Experience working with multimodal data sources (e.g., voice,

imagery, text) for AI model training.

• Proficiency/Experience collecting and interpreting data from interviews.

• Experience using Multi-modal LLMs.

What drives us:

• Impact: we are driven by a shared mission to address a paramount challenge of

our time

• Resolve: we believe that hard work and resilience yield extraordinary outcomes

• Urgency: we are motivated to outpace rapid urbanization and escalating disaster

impacts

Why join RQ:

• Opportunity to be involved in an early-stage startup and build the culture you

want to see.

Chance to pioneer and disrupt the $200B property insurance industry

• Experience firsthand the tangible impact of what you build.

Day to day:

• Collaborate with experienced structural engineers in building a system that

collects data and reasons about buildings as a structural engineer

• Participate in product ideation and development.

• Architect and develop a large geospatial database to host multimodal building

data and context that will grow over time.

• Write, test, document, and review code according to RQ’s development

standards that you would help to define.

• Support the founders in technical integrations with customer systems

• Support customers using the ResiQuant platform and handle domain specific

questions

What we offer:

• Competitive salary commensurate with experience

• Equity in the company as a founding member

• Vibrant tech startup environment

• Competitive company 401(k) program with company matching

• Health insurance

• Working on the challenge of our generation with other passionate people

Ideal Engineer Profile

• Location: San Francisco Bay Area (In person)

• Bachelors: Civil or Mechanical Engineering, or Math with relevant experience

• Masters: Structural engineering, Risk Analysis, or Catastrophe modeling.

• Experience in catastrophe modeling using RMS and/or Verisk models for most

perils

• Experience managing large data set of building and associated data

• Experience in programming and data analysis with Python

• Persistence and adaptability working through setbacks and change of directions.

• Skilled at delegating tasks while staying hands-on with critical development.

• Excellent written and verbal communication skills, with the ability to convey

complex ideas clearly to diverse audiences.