MercorNew York, NYBuild scalable backend and infrastructure systems for reinforcement-learning environments that train AI agents, partnering with researchers and leading AI labs. The role requires strong engineering judgment, hands-on leadership, and experience with ML/LLM concepts and scalable systems.
Build and advance AI agent systems at Perplexity that connect frontier models to tools and environments to perform valuable work for users. Requires strong AI product stack knowledge, Python proficiency, and deep expertise in at least one area such as context engineering, RL/post-training, or browser technologies.
ML Engineer building and optimizing production recommendation, ranking, and personalization systems that integrate LLMs for Perplexity's AI product.
Staff ML Engineer to own the model serving stack for real-time voice inference (STT, TTS, speech-to-speech) on H100/H200 GPUs. Drive latency/throughput optimization using TRT-LLM and SGLang for models like Whisper and Parakeet.
Leads architecture of scalable ML platforms for generative AI across text, image, audio, and video. Drives company-level strategy, mentors engineers, and builds large-scale systems requiring 12+ years experience in ML infrastructure.
Designs, builds, and deploys production AI/ML systems for financial operations like invoice matching and payment reconciliation. Requires 5+ years software engineering with 2+ years applied AI/ML, expertise in LLMs, RAG, and Python/PyTorch.