Achira
Machine Learning Research Engineer (MLRE) - GPUs
About this role
Achira seeks a Machine Learning Research Engineer to optimize deep learning models for molecular simulation on GPU hardware. You'll profile and accelerate PyTorch/JAX implementations while collaborating with scientists to push computational boundaries in AI-driven drug discovery.
What you'll do
- Profile and optimize PyTorch and JAX code for GPU performance without sacrificing accuracy or scientific validity
- Develop GPU-accelerated implementations using CUDA, Triton, Warp, and similar frameworks
- Partner with NVIDIA to integrate their tools and represent company needs in the GPU ecosystem
- Work directly with research scientists to identify high-impact optimization opportunities
- Travel between San Francisco and New York offices for cross-team collaboration
- Own optimization projects end-to-end from design through deployment on distributed infrastructure
What they're looking for
- GPU optimization and CUDA programming
- PyTorch and JAX frameworks
- GPU performance profiling and benchmarking
- High-performance computing frameworks (Triton, Warp)
- Distributed systems and multi-cloud compute
- Scientific software engineering
- Equivariant neural architectures and 3D point clouds (nice to have)
- Collaborative software development
Benefits
- Work on frontier AI problems in molecular simulation and drug discovery
- Access to massive compute, data, and research resources
- Collaborate with world-class ML researchers and scientists
- Well-funded, talent-dense organization
- Flexibility for remote work with exceptional candidates
- Opportunity to shape cutting-edge deep learning architectures
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Achira
Achira builds AI-driven drug discovery platforms using deep learning and foundation models for molecular simulation. The company is hiring Machine Learning Research Engineers and Software Engineers to optimize GPU-accelerated model implementations, design scalable distributed infrastructure, and manage large-scale ML pipelines across cloud environments.
View all jobs at AchiraLikely interview questions
- Walk us through a specific GPU optimization project you've worked on. What profiling tools did you use, and how did you measure success without compromising accuracy?
- Describe your experience with CUDA, Triton, or Warp. Which have you used most, and what are the trade-offs you've encountered?