Hyphen Connect Limited
LLM Pre-training & Distributed Engineer (AI Infrastructure)
Be an Early Applicant
Similar Jobs
eCommerce • Healthtech • Pet • Retail • Pharmaceutical
Leads Chewy’s executive talent lifecycle, including executive onboarding, development, coaching, succession planning, readiness assessment, and leadership experiences. Partners with senior leaders, HR teams, and business stakeholders to design personalized executive programs, improve talent processes, strengthen leadership pipelines, and measure program effectiveness. Establishes governance, operating rhythms, tools, and playbooks to deliver a consistent, high-impact executive experience.
Artificial Intelligence • Internet of Things • Semiconductor
Design and build MLOps infrastructure for production deployment of data, machine-learning models, and software. Develop end-to-end ML pipelines, CI/CD/CT workflows, secure repository strategies, containerized deployments, and automation for Edge AI applications. Collaborate with developers, data scientists, and ML engineers to define tooling, monitor DevOps performance, and improve operational efficiency. Support embedded-system CI/CT, open-source pipelines, and model testing.
Top Skills:
AnsibleAWSAzureCC++DockerElasticsearchGerritGitGithub ActionsGitlab CiGrafanaJenkinsKibanaKubernetesLinuxPackerPrometheusPythonPyTorchTensorFlowTerraform
Artificial Intelligence • Internet of Things • Semiconductor
Leads architecture and hands-on development of distributed AI inference runtime capabilities, including scheduling, batching, KV-cache management, memory allocation, kernel optimization, model execution, and performance benchmarking. Partners with infrastructure, compiler, hardware, cloud, and research teams to improve latency, throughput, reliability, and resource efficiency. Provides technical leadership, conducts reviews, mentors engineers, and establishes performance-engineering practices for production AI workloads.
Top Skills:
Accelerator ProgrammingAi Inference RuntimesC++CompilersDistributed SystemsHigh-Performance NetworkingKernel DevelopmentModel ParallelismOperating SystemsPythonRust
Design and orchestrate large-scale LLM pre-training across 1,000+ GPUs using PyTorch, DeepSpeed, or Megatron-LM. Optimize InfiniBand/RDMA networking and memory to avoid OOM, automate checkpointing and failure recovery for month-long runs, and manage SLURM or Kubernetes GPU clusters. Implement systems-level improvements using C++, CUDA, and Python.
We are seeking a highly skilled LLM Pre-training & Distributed Systems Engineer. This role is essential for orchestrating large-scale machine learning training runs and optimizing distributed infrastructure. The ideal candidate will have a deep understanding of GPU clusters and extensive experience in system engineering to ensure efficient and reliable training processes.
Responsibilities:
- Orchestrate distributed training runs across 1,000+ GPUs using PyTorch, DeepSpeed, or Megatron-LM.
- Optimize networking (InfiniBand/RDMA) and memory management to prevent out-of-memory errors.
- Automate checkpointing and failure recovery during month-long training runs.
Required Skills:
- Deep expertise in 3D parallelism (Data, Tensor, Pipeline).
- Experience managing SLURM or Kubernetes-based GPU clusters.
- Strong systems engineering background (C++, CUDA, Python).
What you need to know about the Seattle Tech Scene
Home to tech titans like Microsoft and Amazon, Seattle punches far above its weight in innovation. But its surrounding mountains, sprinkled with world-famous hiking trails and climbing routes, make the city a destination for outdoorsy types as well. Established as a logging town before shifting to shipbuilding and logistics, the Emerald City is now known for its contributions to aerospace, software, biotech and cloud computing. And its status as a thriving tech ecosystem is attracting out-of-town companies looking to establish new tech and engineering hubs.
Key Facts About Seattle Tech
- Number of Tech Workers: 287,000; 13% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Amazon, Microsoft, Meta, Google
- Key Industries: Artificial intelligence, cloud computing, software, biotechnology, game development
- Funding Landscape: $3.1 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Madrona, Fuse, Tola, Maveron
- Research Centers and Universities: University of Washington, Seattle University, Seattle Pacific University, Allen Institute for Brain Science, Bill & Melinda Gates Foundation, Seattle Children’s Research Institute
