At Achira, we are building a team of world-class scientists, ML researchers, and engineers to work together to move beyond the beaten path in drug discovery. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed.
At Achira, you’ll operate at the frontier scale of massive compute, massive data, and massive ambition. You’ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We’re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success.
We're looking for a rare individual who thrives at the intersection of cutting-edge deep learning architectures and high-performance computing. You will help shape the future of molecular machine learning by engineering high-efficiency implementations of advanced architectures for foundation simulation models, accelerating simulations to the limits of the hardware while maintaining fidelity to the underlying physics.
While we prefer candidates willing to relocate to San Francisco or New York City, we are willing to consider full-time remote candidates of exceptional talent who are willing to travel frequently to our two office sites. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.
Take existing PyTorch and JAX, profile and optimize it without compromising model accuracy, reproducibility, and robustness with respect to scientific objectives.
Develop with frameworks like CUDA, Triton, Warp, etc. to accelerate performance critical code sections.
Liaise with NVIDIA to represent our needs and implement their tooling in our environment.
Work day-to-day with scientists to identify areas of greatest impact, including travel to our SF and NY working groups to collaborate.
Engineer with at least two years professional experience in GPU optimization.
Deep understanding of GPU programming fundamentals.
Solid track record of observable artifacts (e.g., GitHub) showing optimization work.
Experience collaborating on software projects across multi-person teams.
Even if you hit none of these bonus features, we encourage you to apply!
Experience working with multi-cloud distributed compute systems.
Experience working with multi-site distributed company team.
Experience working with equivariant architectures that operate on 3-D point clouds.
Optimize deep learning inference pipelines for microsecond-level latency, evaluating heterogeneous hardware, developing GPU kernels, and improving model efficiency, reliability, and deployment performance.
Build scalable machine learning systems and asynchronous workflows for data generation, model training, and evaluation, enabling research teams to run molecular machine learning experiments at foundation-model scale.
Build and scale Tower’s machine learning research platform, enabling reproducible experimentation, GPU-accelerated training, simulation, and efficient compute across HPC and cloud environments.
Develop machine learning models for autonomous vehicle prediction and planning, turning real-world driving challenges into robust systems that support safe, smooth behavior and integrate into production software.
As a Machine Learning Engineer, you will design and optimize ML applications, run experiments, and collaborate with teams to enhance large-scale systems for real-time data processing.
