AI for the hardest problems in science and engineering: it searches, verifies and compounds expertise. Meet Maxwell, the first Code Execution Model. Backed by Bessemer.
About the role
This is not a pure research role. We are looking for an exceptional engineer who can work close to the model stack, understand deep learning systems end-to-end, and turn ambitious ideas into reliable, high-performance infrastructure. You will work across large-scale training, distributed systems, inference optimization, model evaluation, and deployment — helping us build AI systems that are scalable, efficient, and robust. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Collaborate with cross-functional teams to bring research breakthroughs into production environments
- Build tools, abstractions, and infrastructure that make model training, evaluation, and deployment faster and more reliable
- Analyze and overcome performance and scalability challenges in large-scale distributed training
- Debug complex issues across the full ML stack: data loading, model code, distributed execution, GPU utilization, memory usage, networking, checkpointing, and serving
- Work with modern training and inference frameworks such as Megatron, TorchTitan, PyTorch, and vLLM
- MSc or PhD in AI, Machine Learning or Algorithms
More about this role
We are looking for a Deep Learning Engineer to help design, implement, optimize, and scale the training and inference systems behind our models.
doubleAI is building next-generation reasoning and code-optimization agents that push the frontier of artificial expert intelligence. We are looking for a Deep Learning Engineer to help design, implement, optimize, and scale the training and inference systems behind our models.
This is not a pure research role. We are looking for an exceptional engineer who can work close to the model stack, understand deep learning systems end-to-end, and turn ambitious ideas into reliable, high-performance infrastructure. You will work across large-scale training, distributed systems, inference optimization, model evaluation, and deployment — helping us build AI systems that are scalable, efficient, and robust.
- Collaborate with cross-functional teams to bring research breakthroughs into production environments.
- Build tools, abstractions, and infrastructure that make model training, evaluation, and deployment faster and more reliable.
- Analyze and overcome performance and scalability challenges in large-scale distributed training.
- Debug complex...
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