Achira
Machine Learning Research Engineer (MLRE) - Research
About this role
Achira seeks an ML Research Engineer to advance foundation simulation models for molecular-level drug discovery. You'll design experiments, build scalable training infrastructure, implement novel architectures, and bridge research innovation with production-ready systems at massive compute scale.
What you'll do
- Design and execute experiments to validate hypotheses for foundation model development
- Engineer evaluation metrics and benchmarks enabling rapid model iteration
- Build scalable, reproducible libraries for training, evaluation, and simulation workflows
- Implement model architectures from literature and in-house research for molecular simulation
- Maintain agent-driven research workflows and safety guardrails
- Prepare manuscripts, software artifacts, and datasets for public release
What they're looking for
- Software engineering best practices and reproducible pipeline development
- PyTorch and JAX
- High-performance computing and distributed infrastructure
- ML research and scientific computing
- Geometric deep learning or equivariant architectures (preferred)
- Generative modeling (diffusion, flow matching)
- Open-source contribution experience
- Agent-driven research and active learning
Benefits
- Work at frontier of AI x Chemistry with world-class research team
- Own impactful projects end-to-end from ideation to deployment
- Access to massive compute and data resources
- Hybrid work in San Francisco or NYC offices
- Opportunities to publish and present at conferences
- Well-funded, talent-dense organization
Opens the application — the Jobs AI extension fills it for you. Set up autofill
Opens the official application on the employer’s site. No login required.
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 machine learning project where you built reproducible pipelines and maintained them for research. How did you ensure code quality and documentation?
- Describe your experience with PyTorch and JAX. Which have you used more extensively, and what are the trade-offs you've observed between them?