Machine Learning Research Engineer (MLRE) - Research
Achira · San Francisco Office
About this role
## Machine Learning Research Engineer (MLRE) - Research ### Why Achira At Achira, we’re building a team of world-class scientists, ML researchers, and engineers to advance drug discovery. We’re exploring the next frontier of **AI x Chemistry**—developing **world models for the physical microcosm**—with the goal of making biology at the molecular level something that can be **learned, predicted, and designed**. You’ll work at the frontier scale of **massive compute, massive data, and massive ambition**, owning impactful work **end-to-end** (ideation → architecture → deployment on distributed infrastructure). We value **rigor, speed, execution, and ownership**, and we’re looking for collaborators who share a sense of relentless urgency. ### About the Role You’ll thrive at the intersection of **applied machine learning research** and **rigorous software engineering**. You will: - Advance the state of the art in **foundation simulation models** by implementing and experimenting with internal and literature-sourced ideas. - Collaborate with research teams to **scale ML systems**. - **Train and evaluate** models. - Engineer scientific prototypes into **production**. **Location / Work model:** - Prefer working from our **San Francisco** office. - Highly skilled candidates may be considered for **New York City** with travel to San Francisco as needed. - Both are **hybrid roles**, with at least some time in the office for collaboration. - **Travel** is part of all roles (conferences and corporate on-site activities). ### What You’ll Do - Design and run experiments to test hypotheses on the path to foundation model development. - Engineer meaningful **evals and metrics** to enable rapid model iteration. - Design, build, and maintain scalable, reproducible libraries for training, experimentation, evaluation, and simulation. - Implement model architectures from the literature and in collaboration with in-house researchers to push molecular simulation boundaries. - Enable **agent-driven research and workflows**, with guardrails on agentic tooling. - Help prepare **manuscripts, software artifacts, and datasets** for public release. ### About You - Strong software engineering fundamentals: build **reproducible pipelines** (not just one-off scripts), write documentation, and follow coding best practices. - Track record of observable artifacts (e.g., **GitHub, papers**) in ML or scientific computing libraries. - Solid working knowledge of **PyTorch and JAX** and the modern ML research stack. - Comfortable with **HPC / large-scale compute** environments; able to think at the scale of **hundreds or thousands** of concurrent runs. - Sufficient scientific depth to engage with research questions (via industry experience or a **PhD**). ### Nice to Have - Experience with **equivariant architectures**, geometric deep learning, or **GNNs** (e.g., NequIP, MACE, SchNet, PaiNN, or similar). - Familiarity with generative modeling: **diffusion models**, **flow matching**, **score-based methods**. - Regular involvement in **open-source ML** or scientific computing libraries. - Experience building **agent-driven research**, active learning, and data curation pipelines. *Even if you don’t have all of the nice-to-have items, we encourage you to apply.*
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