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Lead the architecture and delivery of ML and GenAI platforms for JPMorganChase’s Home Lending, turning prototypes into production systems and mentoring AI engineers.
Build and optimize large language models for financial services, focusing on LLM-based methods, training pipelines, and production deployment to improve customer workflows and agent efficiency.
Build and scale deep-learning infrastructure for autonomous-vehicle training, optimizing multi-thousand-GPU clusters, distributed training, and massive video datasets.
Build and optimize distributed GPU training infrastructure for large neural networks, focusing on PyTorch, Megatron-LM, and DeepSpeed to maximize throughput and stability at scale.
Design and deploy AI/ML infrastructures and MLOps solutions on Ubuntu, Kubernetes, and cloud platforms for global enterprises, using open-source tools like Kubeflow and MLFlow.
Principal ML Engineer builds next-gen foundation models from petabyte-scale telematics data to assess driver risk, detect crashes, and improve road safety using transformers and self-supervised learning.
Build and optimize distributed ML training and real-time inference systems to accelerate trading strategies using GPU acceleration and open-source ML frameworks.
Build and maintain Java-based backend services for an AI infrastructure platform that deploys GPU clusters on Kubernetes, handling AI inference, training, and scheduling.
Build and optimize ML infrastructure and tooling to improve training, inference, and GPU utilization for Reddit’s AI systems, using Python and systems languages like Go or Rust.
Design and deploy scalable AI/ML solutions for customers, advising on distributed training and production-scale deployments while bridging technical and business needs.
Build and operate ML infrastructure for a Payments ML platform, focusing on pipelines, training/inference systems, and MLOps tooling to scale AI use cases in payments.
Staff Engineer Tech Lead Manager, ML Acceleration Location: Pittsburgh, Pennsylvania, United States Department: Infrastructure Mission Summary: We are seeking an experienced and visionary Principle Level Tech Lead…
Lead a team to optimize and accelerate ML model training for autonomous vehicles, using PyTorch/JAX and distributed systems to cut development cycles and enable rapid hot-patching.
Build and adapt AI models for early-stage startups, turning research into production systems and improving model performance, reasoning, and efficiency.
Principal AI/ML Engineer to architect and deploy cutting-edge models (LLMs, transformers) and lead AI strategy at high-growth startups in SignalFire’s portfolio.
Secure AI systems by assessing LLM and generative AI security risks, implementing controls for training/inference pipelines, and hardening ML infrastructure against prompt injection, model poisoning, and data exfiltration.
Build and operate the distributed orchestration engine for AWS SageMaker’s Model Factory, enabling scalable foundation-model training and customization workflows across thousands of GPUs and Trainium devices.
Lead AI/ML initiatives at Cisco, defining technical vision and roadmap for AI capabilities in security and observability products while guiding cross-functional teams from architecture to production deployment.
Build and scale Unity Vector’s online ML inference platform, optimizing model serving for low-latency, high-reliability production systems using PyTorch, Triton, Kubernetes, and Ray.
Design and deploy enterprise-grade AI systems—including LLMs, vision models, and robotics—from research to production, ensuring scalability, security, and alignment with business goals.
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