Foundation Models AI Engineer

Amazon Science; Houston (TX); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Science is seeking an AI/ML Engineer to build efficient, stable foundation models for long-horizon agentic workloads, combining model development with systems and hardware optimization. The role emphasizes training, fine-tuning, and serving large-scale models, with a focus on performance and reliability in a robust cloud environment.

Ideal candidates have a strong ML background, experience with PyTorch/JAX, and exposure to GPU architectures and accelerators.

Qualifications

• 3+ years of building models for business applications or equivalent.
• PhD or Master's degree with 4+ years of CS/CE/ML experience is preferred.
• Experience with patents or publications at top-tier conferences or journals.
• Proficient in Java, C++, Python or related language.

Responsibilities

• Design, train, fine-tune, and evaluate custom foundation models.
• Improve model efficiency, stability, latency, throughput and memory usage.
• Build models supporting planning, tool use, memory, self-correction and multi-step task execution.
• Optimize training and inference using CUDA, Triton, and custom kernels.
• Profile and resolve compute, memory, communication and distributed-training bottlenecks.
• Work with GPUs and AWS Trainium and other tensor hardware.
• Develop evaluations for model reliability and long-horizon agent performance.

Skills

Machine learning

Deep learning

Transformers

Foundation models

Education

PhD or Masters in CS/EE/ML

Tools

PyTorch

JAX

Job description

Amazon Science is seeking an AI/ML Engineer to build efficient, stable foundation models for long-horizon agentic workloads, combining model development with systems and hardware optimization. The role emphasizes training, fine-tuning, and serving large-scale models, with a focus on performance and reliability in a robust cloud environment.

Ideal candidates have a strong ML background, experience with PyTorch/JAX, and exposure to GPU architectures and accelerators.