About the job:- Research, design, and build next-generation multimodal AI capabilities for intelligent consumer devices, spanning speech/audio, vision, language, touch, gesture, motion, and contextual sensing
- Identify high-impact research problems and own projects from early exploration through model development, evaluation, and product-oriented validation
- Design, train, fine-tune, and evaluate deep learning and foundation models for multimodal perception, reasoning, interaction, and generation
- Develop new approaches for reasoning across modalities to enable more natural, adaptive human-machine interaction
- Rapidly prototype research ideas, reproduce state-of-the-art methods, and evolve promising concepts into robust demonstrations
- Build datasets and evaluation methodologies, run rigorous experiments, and establish metrics reflecting both model performance and real interaction quality
- Optimize models for real-world constraints (latency, memory, power, privacy, robustness, on-device vs. cloud) and collaborate across research, engineering, and product teams
- Contribute reproducible code, documentation, and IP through publications, patents, or open-source projects when appropriate
The
total target annual compensation for this position ranges from $106,000 - $156,000 depending on education, experience, and demonstrated expertise.
About the ideal candidate:- Master's or PhD in CS, EE/CE, AI, ML, Statistics, Math, HCI, or related field (exceptional Bachelor's candidates considered)
- Strong foundation in ML/deep learning with hands-on experience in PyTorch, TensorFlow, JAX, or similar
- Technical depth in multimodal learning, speech/audio, computer vision, NLP, generative AI, or foundation models
- Experience with LLMs/VLMs, agentic AI, multi-agent systems, or context-aware intelligent systems is a strong asset
- Track record turning research into working systems: prototypes, open-source, patents, publications, or shipped products
- End-to-end ML development experience: data prep, training, evaluation, debugging, optimization, deployment
- Understanding of practical AI trade-offs (quality, latency, compute, memory, robustness) and on-device/resource-constrained deployment
- 3+ years relevant research or industry experience (including graduate research), with strong Python (Typescript / Kotlin, C++/Java a plus) and collaboration skills across teams
