СШАApplied AI Engineer (Product)
1 мес. назад
СШАSeniorOnsite
llmvlmworld modelsmodel routingretrievalmemory managementhuman-in-the-loop
Build agentic workflows and intelligence products combining physics-informed models, AI, and ultraspectral data for operational decision-making.
О компании
- Welcome to Matter, where we are building the future of vision AI: pairing a world-first sensor that sees molecular chemistry, temperature, and 3D shape with a Large World Model that will be the most powerful intelligence engine for the physical world. This system doesn't just see what something looks like; it understands everything from a single pixel. We call this Superintelligent Vision.
- Our team has delivered technologies to Mars for NASA/JPL, designed advanced sensors for U.S. Defense, and built core infrastructure at OpenAI. We are now building the next generation of space- and airborne-based sensing systems.
Обязанности
- Matter is hiring a Product Intelligence Engineer to turn physics-informed models, general-purpose AI, and ultraspectral data into secure, evidence-backed intelligence products. Reporting to Ignacio Cases Martin, this hands-on individual contributor will build agentic workflows, retrieval and tool integrations, reports, and human-in-the-loop experiences that help customers make operational decisions.
- Build production workflows that combine physics-informed models, LLMs, VLMs, world models, scientific code, data services, and deterministic rules through stable interfaces.
- Design durable execution across state, planning, model routing, retrieval, memory, tool use, retries, timeouts, checkpoints, human approval, and recovery.
- Create evidence-backed outputs that distinguish source data, model inference, system-generated synthesis, user input, and unresolved uncertainty.
- Partner with product and design to build workflows for review, correction, comparison, approval, reporting, and recovery.
- Build context, retrieval, memory, and indexing patterns that respect permissions, freshness, versioning, retention, and evidence lineage.
- Evaluate real system behavior across task success, groundedness, scientific validity, safety, latency, cost, and human intervention.
- Customers receive timely, evidence-backed answers and workflows that support real operational decisions.
- Agentic capabilities behave predictably across failures, version changes, unavailable dependencies, and human-review boundaries.
- Reusable product-intelligence components reduce one-off integration work without obscuring user control or scientific evidence.
Требования
- To comply with U.S. export regulations, applicants must be one of the following:
- A U.S. citizen or national
- A lawful permanent resident (green card holder)
- Eligible to obtain required authorizations from the U.S. Department of State
Будет плюсом
- Experience with scientific, geospatial, industrial, defense, autonomy, or other high-consequence intelligence systems.
- Experience building multimodal applications that combine imagery, maps, time series, documents, structured data, or sensor streams.
- Experience with agent evaluation, constrained generation, program synthesis, durable workflow systems, or mixed cloud and edge architectures.
- Experience working closely with product and design teams to ship customer-facing capabilities.
Условия
- This role is based in San Francisco, CA, and requires onsite work.
- At Matter, we believe in rewarding high performance and providing the support you need to thrive. Our compensation and benefits package includes:
- Competitive compensation based on experience
- Early-stage equity package
- 100% employer-paid health, dental, and vision coverage
- Opportunity to work on novel sensing, data, and AI systems with real-world deployment paths to the largest industries in the world
Другое
- Experience building production AI applications, agent systems, workflow engines, backend services, or complex software products.
- Strong hands-on engineering skills across APIs, stateful services, databases, asynchronous execution, testing, debugging, and production operation.
- Practical experience with LLMs or multimodal systems, retrieval, memory, tool use, model routing, structured generation, and common failure modes.
- Product judgment and the ability to connect technical design choices to the user decision or workflow being improved.
- Experience building systems with security, permissions, data isolation, auditability, or human-approval requirements.