Applied Scientist - Multimodal AI Models

Job Description

Join us at the frontier of AI interfaces. In this role, you’ll own the intersection of multimodal AI, real-time voice, vision-language models, mobile-use agents, memory, and on-device intelligence. You’ll architect and build AI systems that understand physical context through camera, microphones, and device state—then turn that understanding into useful action. You’ll work across the full stack from cloud to device, taking problems from idea to execution with real ownership.

Key Responsibilities

• Build agentic systems with multi-step task execution, planning and decomposition, and state management
• Apply quantization (PTQ and QAT), pruning, and architecture search to meet per-product size, latency, and power budgets
• Design and implement vision-language model applications including prompt architecture, structured output, context management, and visual understanding
• Scale simulation and scaffolding environments for agentic RL including code execution sandboxes, computer use environments, and tool-calling harnesses
• Build evaluation infrastructure including task suites, automated scoring, regression harnesses, and success/latency/cost measurement
• Design and implement data pipelines for capture, schema design, labelling, and privacy-safe dataset curation
• Integrate real-time voice (ASR and TTS), optimize streaming interaction, and engineer end-to-end latency
• Partner with mobile and hardware engineers to move capability from cloud onto device
• Own a surface area—take real ownership of problems and drive them from idea to execution

Qualifications

• Minimum 3+ years of professional experience (not hiring new graduates)
• Deep expertise in agentic systems, multi-step task execution, planning, decomposition, and state management
• Applied experience with vision-language models including prompt architecture, structured output, and context management
• Strong understanding of reinforcement learning: policy optimization, reward design, exploration, and environment design
• Experience with model adaptation including fine-tuning workflows, dataset construction, and running models under tight compute/memory budgets
• Proficiency with quantization techniques (PTQ and QAT), pruning, and architecture search for efficient inference
• Hands-on experience building evaluation infrastructure and automated scoring systems
• High agency, ownership-driven mindset—ship without being asked, solve hard problems because you want the answer