Machine Learning Engineer
Core
Design and build the high-level architecture of a next-generation MLOps platform for deploying, scaling, and monitoring GenAI models (LLMs, speech, vision, diffusion) across cloud and on-prem environments.
Role type
Senior IC Machine Learning Infrastructure Engineer (MLOps Platform)
Builds
Scalable, reliable, GPU-accelerated ML workflows and MLOps infrastructure
Domain
Generative AI, Cloud Infrastructure, Distributed Systems
Deliverable
infrastructure
Required skills
System design, Distributed systems, GPU-based ML workloads, Software engineering fundamentals, Infrastructure-as-code, Cloud platforms, ML fundamentals, Multi-step pipeline orchestration, Linux internals, Networking, Performance tuning
Preferred skills
Modern inference stacks (TensorRT, Triton, vLLM/TGI, SGLang), Model optimization, CUDA concepts, LLM/VLM/ASR pipeline experience, High-availability system design
Technologies
Terraform, Ansible, AWS, GCP, Azure, Linux, Docker, CI/CD, GitHub workflows
Responsibilities
Design and implement core architecture for GPU-accelerated workloads, Formalize heterogeneous ML workload abstractions, Build internal systems for continuous deployment across multi-cloud, Create frameworks for reliability and observability, Develop internal tooling for benchmarking and deployment, Troubleshoot complex systems and GPU behavior
Seniority
Senior, hands-on IC
