Cox Exponential

Cox Exponential

Founding Engineer, AI Infra

SF Bay Area, USA

Sponsorship not specifiedDetected 42 days ago
PythonGoRustC++AlgorithmsVector DatabasesKubernetesTerraformPrometheusGrafanaMachine LearningPyTorchData EngineeringLLMsSystems EngineeringResearch

About the role

  • You will sit at the intersection of systems engineering and applied ML, building specialized infrastructure that keeps large language and multimodal models fast, reliable, and cost-effective.
  • You will partner with research, product, and infra teams to ship production-ready platforms for training and serving AI at scale.

Responsibilities

  • Training & RL robustness: Build scalable, stable training and RL pipelines with strong reproducibility, observability, and debuggability.
  • Serving & inference optimization: Design and tune high-throughput, low-latency model serving systems, including quantization, caching, and speculative decoding.
  • Scalability & infrastructure: Own end-to-end training and inference infrastructure - from data ingestion and checkpointing to multi-GPU and multi-cloud orchestration.
  • Requirements 5+ years building or operating ML infrastructure at scale, ideally supporting large language or multimodal models.

Benefits

  • Bonus Points Experience deploying open-source LLMs (Llama 3, Qwen, DeepSeek) or training custom foundation models.
  • Background in reinforcement learning, evaluation harnesses, or alignment tooling that hardens production AI systems.

Company info

  • About Goaly At Goaly, our mission is to make custom AI affordable for every business.
  • Our founding team comes from the front lines of top AI labs and tech giants (Meta MSL, TikTok AI, Google DeepMind, xAI, Microsoft Research, etc.), where we built large-scale training infrastructure powering trillion-parameter models and scaled GenAI models to a global user base.
  • a platform that makes training and adapting custom AI affordable for all modern companies, not just Big Tech.

This listing is sourced directly from Cox Exponential's careers page and normalized into a canonical job model.