Machine Learning Team Lead

Stand; San Francisco (CA); On-site; USD 250,000 - 295,000; Full time

Benefits offered by this job

Above-market Health, Dental, and Vision coverage

Weekly lunch stipend

Flexible time off + holidays

401(k) plan

Commuter benefits

PAT & MAT Leave

Short-Term and Long-Term Disability

Monthly team gatherings

In-office perks

Job summary

Stand in San Francisco is seeking an MLE Team Lead to guide the Machine Learning Engineering team in developing and deploying cutting-edge AI solutions. You will work directly with the Chief Science Officer and own key projects while nurturing team growth and efficiency.

The ideal candidate will have a strong background in machine learning, experience in leadership, and a solid understanding of physics-based modeling.

This role offers a competitive salary and benefits including health coverage, flexible time off, and a meaningful equity grant.

Qualifications

• Proficiency with modern ML tooling and infrastructure.
• Experience leading engineers and technical initiatives.
• Ability to operate across disciplines connecting technical development to business objectives.

Responsibilities

• Lead the Machine Learning Engineering sub-team.
• Manage and grow the team with 1-on-1s and feedback.
• Design, build, and deploy machine learning systems.

Skills

Machine Learning

Physics-based modeling

Project management

Strong communication

Data pipelines

Education

Experience leading engineers

Experience with simulation and scientific computing

Tools

Modern ML tooling and infrastructure

TensorFlow

PyTorch

Job description

Why Join Stand: At Stand, you’ll help build a new class of global property protection. We use advanced physics and AI to model catastrophic risk at the asset level, then automate underwriting and mitigation before loss occurs. Insurance is simply the current delivery mechanism. The real product is a scalable risk engine, our Stand World Model https://frontier.standinsurance.com/.

We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices.

Leadership: Our leadership team includes former successful founders and CEOs from Metromile, PolicyGenius, WePay, and HotelTonight, bringing deep experience in building and scaling high-growth companies.

Background: The property insurance industry is built to price loss after it happens. It relies on coarse proxies, backward-looking data, and manual processes, then accepts damage as unavoidable.

Stand takes a different approach. We simulate how real-world catastrophes affect individual properties, translate that into actionable decisions, and automate the business around it. The result is a platform that can underwrite what others can’t and operate with far less friction.

Role Summary

As the MLE Team Lead on the Applied Science team, you will lead the Machine Learning Engineering sub-team as it develops and deploys Stand's flagship AI capabilities spanning physics-informed machine learning, digital twins, computer vision, and spatial intelligence. You will own the technical direction, planning, and execution of critical AI initiatives, ensuring they align with business priorities, ship on schedule, and deliver measurable outcomes.

This is a player-coach role, combining direct technical work and the leadership work around it: people management, project planning, cross-team coordination, and process. Reporting directly to the Chief Science Officer, you will own key projects yourself while ensuring the broader MLE team is operating effectively, growing, and delivering real impact. You are the person who looks around corners, sees what the business needs, and turns 'the business needs X' into 'the team builds Y.'

You will partner across Applied Science and the business to transform research and emerging technologies into scalable systems that directly influence underwriting, pricing, mitigation, inspection, and customer decision-making.

Key Initiatives

• Advancing physics-informed, AI-driven solvers and surrogate architectures
• Advancing multimodal models, data augmentation, sensor fusion, and digital twin capabilities
• Driving R&D programs through to validation, deployment, and business adoption
• Building production-ready AI systems that accelerate, automate, and scale risk analytics

What You'll Do

• Lead the Machine Learning Engineering sub-team, defining priorities, coordinating execution, and unblocking the team to deliver on critical AI initiatives
• Manage and grow the team, running 1-on-1s and growth conversations, giving direct and timely feedback, managing performance, and mentoring engineers as the team scales
• Design, build, and deploy machine learning systems spanning physics-informed AI, digital twins, computer vision, and spatial intelligence, contributing directly to core components
• Own projects end-to-end, from problem definition and prototyping through production deployment, adoption, and ongoing performance
• Extend state-of-the-art models and surrogate architectures to accelerate simulation and risk analytics workflows
• Guide, support, and build scalable ML infrastructure, including data pipelines, training systems, evaluation frameworks, and production monitoring
• Improve how the team works, creating process improvements and maintaining traceability
• Drive cross-functional alignment, coordinating across Applied Science and the business and clearly communicating modeling decisions, tradeoffs, and status
• Set a multi-year vision for the MLE team's impact and articulate how its work moves the business

Core Skills (Must-Haves)

• Proficiency with modern ML tooling and infrastructure
• Experience leading engineers and technical initiatives, delivering complex projects through others as well as through direct individual contribution
• Strong project ownership and execution: planning, prioritization, stakeholder coordination, and delivery of complex technical programs from concept through production
• Experience combining physics-based modeling and machine learning, including simulation, scientific computing, surrogate modeling, and/or physics-informed AI approaches
• Ability to operate across disciplines, connecting technical development to business objectives and customer impact, and articulating those links to the team
• Strong, succinct communication and the judgment to balance research depth, delivery timelines, and business impact
• Highly self-motivated, proactive, and adaptable; comfortable in fast-paced, ambiguous environments where problems, interfaces, and priorities evolve

Nice to Haves

• Prior experience as a people manager, specifically in high-growth environments
• Experience with computer vision, multimodal learning, or spatially-aware architectures
• Familiarity with building agentic systems and LLM-powered workflows
• Experience in startups or zero-to-one technology development
• Knowledge of geospatial, remote sensing, or Earth observation datasets and systems

Compensation

The annual base salary range for full-time employees in this position is $250,000 to $295,000 plus meaningful Equity Grant. Compensation decisions are dependent on several factors including, but not limited to, an individual’s qualifications, location where the role is to be performed, internal equity, and alignment with market data.

Benefits

• Above-market Health, Dental, and Vision coverage
• Weekly lunch stipend
• Flexible time off + holidays
• 401(k) plan
• Commuter benefits
• PAT & MAT Leave
• Short-Term and Long-Term Disability
• Monthly team gatherings
• In-office perks

Work Authorization

Candidates must be authorized to work in the U.S. Stand does not sponsor new work visas. We can consider candidates on TN visas, O-1A visas, or H-1B transfers with three years or more remaining.

Equal Opportunity Employment

Stand is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. We believe that diversity enriches the workplace, and we are committed to growing our team with the most talented and passionate people from every community.

We are committed to providing reasonable accommodations for qualified individuals. If you require assistance

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.