12 months ago
San Francisco, CA, USA or New York, NY, USASenior
Base Salary
$165k - $259k/yr
Responsibilities
- Architect, implement, and optimize distributed compute infrastructure for ML data processing, training, and fine-tuning.
- Improve cluster observability, scheduling, and CPU, GPU, and TPU resource utilization.
- Research and implement cost-efficient compute solutions including spot instances, auto-scaling, and multi-cloud strategies.
- Develop monitoring, debugging, and performance-tuning tools for large-scale ML workloads.
- Collaborate with ML engineers to accelerate training pipelines and reduce bottlenecks.
- Evaluate and strategically apply distributed-computing technologies such as Ray, Kubernetes, Spark, and Slurm.
Requirements
- Extensive experience building or working with distributed computing frameworks such as Ray, Dask, or Celery.
- Strong understanding of parallel computing, job scheduling, and resource management.
- Ability to identify and resolve distributed-systems performance issues involving profiling, bottlenecks, and network overhead.
- Experience implementing solutions with AWS, GCP, or Azure and cluster orchestration using Kubernetes or Slurm.
- Familiarity with PyTorch, TensorFlow, or JAX and MLOps practices including model deployment and GPU performance monitoring.
- Must be able to verify identity, work authorization, and employment eligibility in the United States.
Benefits
- Work with scientists, ML researchers, and engineers on drug discovery.
- Work on large-scale ML infrastructure and distributed computing.
- Own projects end-to-end from ideation through deployment.
- United States work authorization and employment eligibility verification are required.
Tech Stack
Categories
About Achira
Achira builds atomistic foundation simulation models that simulate molecular interactions to support drug discovery and preclinical research for biotech and pharmaceutical teams. The privately held company, founded in 2024 and based in San Francisco, offers its technology as software and research collaborations. Its work combines AI with large-scale physics-based simulation to accelerate target validation and lead optimization.