Stand
Machine Learning Engineer - Multimodal Modeling
San Francisco · Full-time
No sponsorship$250k-$295kDetected 3 days ago
Machine LearningDeep LearningData EngineeringLLMsRoboticsLeadershipCommunicationAdaptabilityUnderwritingRemote Sensing
About the role
- Designing and training multimodal model architectures that jointly reason over physical, spatial, and business-context data
- This is a hands-on, high-ownership position on the Machine Learning team within Stand Applied Science.
Responsibilities
- Own projects end-to-end, from problem definition and prototyping through production deployment, adoption, and ongoing performance monitoring
- Develop rigorous evaluation frameworks that weigh model judgments against real business outcomes
- Build on and extend scalable ML infrastructure
- Partner with Stand's Platform team on the model-harness interface
- Drive cross-functional alignment, communicating decisions, tradeoffs, and status
- Why Join Stand https://www.standinsurance.com/careers/why-stand/: At Stand, you'll help build a new class of global property protection.
Requirements
- Candidates must be authorized to work in the U.S. Stand does not sponsor new work visas.
Skills
- A record of bringing models of this class to production: training at scale, evaluation, deployment, and iteration on live systems
- Experience training or fine-tuning LLMs, including tool use, agentic workflows, or post-training methods
- Strong project ownership and execution: planning, prioritization, and delivery of complex technical work
- Ability to operate across disciplines, connecting technical development to business objectives
- Strong, succinct communication and judgment to balance R&D, delivery timelines, and business impact
- Highly self-motivated, proactive, and adaptable; comfortable in fast-paced, ambiguous environments
- Nice to Haves:
- Experience with retrieval and embedding systems: vector search and similarity in latent space
- Experience with geometric deep learning: point clouds, meshes, and spatially-aware architectures
- Familiarity with physics-informed AI and surrogate modeling across a number of domains
- Experience in startups or zero-to-one technology development
- Knowledge of geospatial, remote sensing, or Earth observation datasets
Compensation
- The annual base salary range for full-time employees in this position is $250,000 to $295,000 + meaningful Equity Grant.
- Compensation decisions are based on several factors, including an individual's qualifications, the location where the role is performed, internal equity, and alignment with market data.
Benefits
- Above-market Health, Dental, and Vision coverage
- Weekly lunch stipend
- Flexible time off + holidays
- Commuter benefits
- PAT & MAT Leave
- Short-Term and Long-Term Disability
- In-office perks
Company info
- Experience applying ML to complex physical systems. We are agnostic to the domain: atmospheric, molecular, protein, robotics, fluid dynamics, or other physics-grounded modeling all carries over
- 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.
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.
Visa & 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.
This listing is sourced directly from Stand's careers page and normalized into a canonical job model.