Achira
SWE - Distributed
San Francisco Office
Sponsorship not specifiedDetected 287 days ago
Distributed SystemsAWSGCPAzureKubernetesMachine LearningTensorFlowPyTorchSparkMLOpsResearchCollaboration
About the role
- Atomistic Foundation simulation models (FSMs) as world models of the physical microcosm span machine learning interaction potentials (MLIPs), neural network potentials (NNPs), and diverse classes of generative models.
- We're seeking a Software Engineer passionate about distributed computing and its applications in machine learning.
- Your expertise will ensure our compute clusters are efficient, observable, cost-effective, and reliable-helping us push the boundaries of ML development.
Responsibilities
- Architect & Build: Design, implement, and optimize distributed compute infrastructure for ML data processing, training, and fine-tuning.
- Optimize & Monitor: Improve cluster observability, scheduling, and resource utilization (CPU/GPU/TPU).
- Compute Efficiency: Research and implement cost-efficient compute solutions (spot instances, auto-scaling, multi-cloud strategies).
- Tooling: Develop tools for monitoring, debugging, and performance tuning of large-scale ML workloads.
- Collaboration: Collaborate with ML engineers to accelerate training pipelines and reduce bottlenecks.
- Own impactful work end-to-end - from ideation to architecture to deployment on large-scale infrastructure.
Requirements
- You have a good grasp of parallel computing, job scheduling, and resource management.
- You are familiar with popular ML frameworks (PyTorch, TensorFlow, or JAX) and MLOps best practices such as model deployment and GPU performance monitoring
Skills
- Stay current with emerging technologies in distributed computing (e.g., Ray, Kubernetes, Spark, Slurm) and apply them strategically.
Company info
- Achira is a company which lives and breaths on computation, facile access at the lowest cost for our uniquely suited workloads is a mission critical endeavor.
This listing is sourced directly from Achira's careers page and normalized into a canonical job model.