Multimodal

We seek exceptional individuals who: Bring proven expertise, demonstrated through impactful publications or technical leadership on high-scale projects. Possess strong analytical skills, attention to detail, and a data-driven approach to decision-making. Have experience with large-scale distributed systems and scalable architectures. Thrive in fast-paced, collaborative environments and embrace innovation. Bachelor's Degree in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Master's Degree in in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or related technical discipline AND 8+ years technical engineering experience with coding in languages including, but not limited to, Python and common data libraries (Pandas, NumPy, etc.) OR Bachelor's Degree in AI, Computer Science, Data Science, Statistics, Physics, Engineering, or related technical discipline AND 12+ years technical engineering experience with coding in languages including, but not limited to, Python and common data libraries (Pandas, NumPy, etc.) OR equivalent experience. Experience with large-scale AI systems — design and deployment of distributed architectures, multimodal or conversational models; proficiency with ML frameworks (e.g., PyTorch, TensorFlow) and cloud/HPC environments (e.g., Azure). Expertise in data engineering for foundation models — multimodal dataset design, curation, annotation pipelines, quality evaluation, bias detection, and understanding of privacy, compliance, and Responsible AI principles. Background in LLM interaction and deployment — practical work in prompt engineering, safety-aligned evaluation, and integration of conversational AI into production systems. Cross-functional collaboration and communication — ability to produce clear technical documentation, partner with engineering, product, and design teams, and contribute to knowledge sharing; demonstrated application of emerging AI technologies and best practices. Develop algorithms, design model architectures, conduct experiments, champion measurement and evaluation, innovate datasets and data pipelines. Improve training and deployment efficiency, paying careful attention to detail, persevering, and learning from everyone's attempts whether successful or not. Follow a rigorous data-driven approach grounded in meticulous ablation studies and scientific analysis. Innovate and iterate over ideas, prototypes, and product. Collaborate closely with teams on infrastructure, data engineering, pre-training, post-training, and product feedback. Advance the AI frontier responsibly. Embody our culture and values.