Researcher: Inference

About Cartesia

Our mission is to build the next generation of AI: ubiquitous, interactive intelligence that runs wherever you are. Today, not even the best models can continuously process and reason over a year-long stream of audio, video and text—1B text tokens, 10B audio tokens and 1T video tokens—let alone do this on-device.

We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences.

We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI.

The Role

• Conduct cutting-edge research to improve the efficiency, scalability, and robustness of inference for state-of-the-art AI models across various modalities, including audio, text, and vision.

• Design and optimize inference pipelines to balance performance, latency, and resource utilization in diverse deployment environments, from edge devices to cloud systems.

• Develop and implement novel techniques for efficient model execution, including quantization, pruning, sparsity, distillation, and hardware-aware optimizations.

• Explore speculative decoding methods, caching strategies, and other advanced techniques to reduce latency and computational overhead during inference.

• Investigate trade-offs between model quality and inference efficiency, designing architectures and workflows that meet real-world application requirements.

• Prototype and refine methods for stateful inference, streaming inference, and task-specific conditioning to enable new capabilities and use cases.

• Collaborate closely with cross-functional teams to ensure inference research seamlessly integrates into production systems and applications.

What We’re Looking For

• Deep expertise in optimizing inference for machine learning models, with a strong understanding of techniques such as speculative decoding, model compression, low-precision computation, and hardware-specific tuning.

• Strong programming skills in Python, with experience in frameworks like PyTorch, TensorFlow, or ONNX, and familiarity with inference deployment tools such as TensorRT or TVM.

• Knowledge of hardware architectures and accelerators, including GPUs, TPUs, and edge devices, and their impact on inference performance.

• Experience in designing and evaluating scalable, low-latency inference pipelines for production systems.

• A solid understanding of the trade-offs between model accuracy, latency, and computational efficiency in deployment scenarios.

• Strong problem-solving skills and a passion for exploring innovative techniques to push the boundaries of real-time and resource-constrained inference.

Nice-to-Haves

• Experience with speculative decoding and other emerging techniques for improving inference performance.

• Familiarity with stateful or streaming inference techniques.

• Background in designing hybrid architectures or task-specific models optimized for inference.

• Early-stage startup experience or a track record of developing and deploying efficient inference systems in fast-paced R&D environments.

Our culture

🏢 We’re an in-person team based out of San Francisco. We love being in the office, hanging out together and learning from each other everyday.

🚢 We ship fast. All of our work is novel and cutting edge, and execution speed is paramount. We have a high bar, and we don’t sacrifice quality and design along the way.

🤝 We support each other. We have an open and inclusive culture that’s focused on giving everyone the resources they need to succeed.

Our perks

🍽 Lunch, dinner and snacks at the office.

🏥 Fully covered medical, dental, and vision insurance for employees.

🏦 401(k).

✈️ Relocation and immigration support.

🦖 Your own personal Yoshi.