Overview

We are seeking a Senior AI Research Scientist with deep expertise in modern foundation models and advanced neural-network architectures.

Responsibilities

• Design, implement, and evaluate new neural-network architectures for language, reasoning, multimodal generation, and agentic workloads.
• Research the strengths and limitations of Transformers, SSMs, diffusion models, recurrent architectures, mixture-of-experts models, and hybrid designs.
• Develop architectures that combine attention, state-space mechanisms, recurrence, memory, sparse routing, retrieval, and modular components where appropriate.
• Explore efficient long-context methods, external and recurrent memory, adaptive computation, speculative execution, and improved reasoning techniques.
• Investigate diffusion and flow-based approaches for text, image, audio, video, structured data, and multimodal generation.
• Translate promising research papers and mathematical concepts into reproducible prototypes and production-relevant experiments.
• Design scalable pipelines for creating high-quality synthetic examples, reasoning traces, preferences, critiques, simulations, and task-specific training data.
• Develop teacher-student, self-training, rejection-sampling, curriculum-learning, process-supervision, and knowledge-distillation approaches.
• Evaluate alignment and post-training methods such as supervised fine-tuning, preference optimization, reinforcement learning, and AI-generated feedback.
• Build filtering, scoring, deduplication, provenance, contamination-detection, and quality-control systems for synthetic datasets.
• Study and reduce the risks of feedback loops, bias amplification, hallucinations, overfitting, reward hacking, and model collapse caused by poorly controlled synthetic data.
• Establish methods for combining synthetic, public, licensed, customer-authorized, and human-generated data while maintaining privacy and traceability.
• Develop algorithms that partition, route, and execute neural-network workloads across heterogeneous devices and infrastructure.
• Research tensor, pipeline, expert, sequence, and context parallelism for inference in resource-constrained and geographically distributed environments.
• Design efficient methods for model sharding, layer placement, distributed KV-cache management, cache-aware scheduling, and dynamic workload migration.
• Improve inference across mixed hardware, including data-center GPUs, consumer GPUs, CPUs, NPUs, Apple Silicon, integrated graphics, edge devices, and browser-based runtimes where appropriate.
• Develop routing and scheduling strategies that account for memory capacity, bandwidth, latency, thermal limits, energy use, device availability, privacy rules, and workload priority.
• Create resilient inference methods that tolerate node loss, unreliable connectivity, changing resource availability, and partial system failure.
• Explore decentralized or collaborative neural networks in which multiple devices jointly execute models without requiring all data or model components to reside in one location.
• Advance compression and acceleration methods, including quantization, sparsity, pruning, distillation, speculative decoding, optimized kernels, and hardware-aware model design.
• Form clear hypotheses, define baselines, design ablation studies, and run statistically sound experiments.
• Create evaluation suites covering accuracy, reasoning, robustness, safety, privacy, latency, throughput, memory, energy use, resilience, and cost.
• Identify where conventional benchmarks fail to predict real-world performance and develop task-relevant evaluations.
• Analyze quality-versus-efficiency tradeoffs and produce evidence that guides architecture and product decisions.
• Maintain reproducible research code, experiment records, model cards, dataset documentation, and technical reports.
• Monitor relevant research and clearly communicate which developments are promising, immature, or unsuitable for systems.
• Help define the company's research roadmap, technical strategy, and standards for scientific quality.
• Collaborate with engineering teams to move successful research from prototype to reliable production systems.
• Work with product leaders to connect research goals to customer needs, deployment constraints, and measurable business or social outcomes.
• Mentor researchers and engineers, review experimental designs, and raise the technical quality of the broader team.

Qualifications

• Hands-on work with model parallelism, expert parallelism, distributed KV caches, inference schedulers, or heterogeneous clusters.
• Knowledge of inference engines and optimization stacks such as vLLM, SGLang, TensorRT-LLM, DeepSpeed, Ray, Triton, CUDA, ROCm, MLX, ONNX Runtime, WebGPU, or similar technologies.
• Familiarity with alignment and post-training techniques, including preference optimization, reinforcement learning, AI feedback, red teaming, and safety evaluation.
• Knowledge of privacy-preserving, federated, decentralized, on-device, or edge machine learning.
• A record of leading ambiguous research projects and helping others turn exploratory work into reliable systems.
• PhD in computer science, machine learning, artificial intelligence, applied mathematics, computational science, electrical engineering, physics, or a closely related field; or equivalent research and industry experience.
• Typically 5+ years of relevant research or advanced development experience, including substantial work with modern foundation models or generative AI.
• Deep expertise in at least two of the following: LLM architecture, SSMs, hybrid neural architectures, diffusion or flow-based models, synthetic-data training, model optimization, or distributed inference.
• Strong understanding of the mathematics underlying deep learning, including optimization, probability, linear algebra, numerical methods, sequence modeling, and representation learning.
• Demonstrated ability to convert research ideas into working implementations and evaluate them against strong baselines.
• Advanced proficiency with Python and at least one major machine-learning framework, such as PyTorch or JAX.
• Understanding of memory, compute, bandwidth, latency, and numerical-precision constraints in modern AI systems.
• Strong experimental judgment, technical writing, and communication skills.
• Evidence of meaningful research impact through publications, patents, open-source contributions, deployed systems, or equivalent technical achievements.