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 machine learning systems architecture and distributed computing. You will help architect the future of molecular machine learning by enabling our scientific teams to flexibly conduct experiments at scale, pushing the boundaries of foundation simulation models.
While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.
Build and maintain robust multi-stage asynchronous workflows for running data generation, training, and evaluations for our machine learning stack.
Rationalize machine learning systems design and software architecture.
Identify blockers and build solutions that scale to the size of foundation models.
Operate as the glue between research scientists and the infrastructure team.
At least two years relevant industry experience.
Highly fluent in and enthusiastic about PyTorch and JAX.
Used to thinking in asynchronous primitives.
Strong views on library design: clean abstractions, minimal surface area, consistency.
Solid track record of observable artifacts (e.g., GitHub) showing clear, well-documented code.
ML generalist who knows what scalable, reliable ML systems look like.
Even if you hit none of these bonus features, we encourage you to apply!
Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar), and/or ML-assisted drug discovery.
Experience building in declarative workflow orchestration frameworks like Flyte, Dagster, etc.
Lack of fear around interacting with quantum chemical scientists and their data pipelines.
achira