Application Guide
How to Apply for ML Research Developer
at LawZero
🏢 About LawZero
LawZero is focused on a novel AI safety agenda, positioning itself at the intersection of cutting-edge machine learning research and critical safety considerations. Working here means contributing to foundational research that could shape how large models are developed and deployed responsibly, offering a unique opportunity to work on high-impact problems with a specialized team.
About This Role
As an ML Research Developer at LawZero, you'll bridge research and engineering by implementing and optimizing workflows for training and inference with very large models, directly supporting AI safety research. Your work will accelerate experiments from toy scenarios to large-scale projects, making you a key enabler of the team's research velocity and practical impact.
💡 A Day in the Life
A typical day might involve collaborating with ML research scientists to debug or optimize a training pipeline for a large model, implementing new workflow tools in PyTorch or TensorFlow, and documenting best practices for the team. You could also be designing experiments for simulated environments or managing cloud resources to ensure efficient compute usage across projects.
🚀 Application Tools
🎯 Who LawZero Is Looking For
- Has 3+ years of hands-on experience designing complex ML workflows on high-performance computing hardware using PyTorch, TensorFlow, or JAX, with a proven ability to scale experiments.
- Demonstrates experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker/Kubernetes) to optimize resource usage in research environments.
- Possesses an advanced degree (MSc or higher) in ML or equivalent industry experience, coupled with a track record of collaborating effectively with research scientists to translate ideas into robust implementations.
- Is proactive in establishing and documenting best practices for ML development workflows, showing an ability to stay current with advancements in both ML research and software engineering tools.
📝 Tips for Applying to LawZero
Highlight specific examples where you designed or implemented ML workflows for large-scale model training or inference, quantifying improvements in efficiency or resource usage.
Tailor your resume to emphasize experience with AI safety, large language models, or related research areas, as LawZero is explicitly focused on a 'novel AI safety agenda.'
Detail your collaboration with research scientists—mention projects where you accelerated research by building tools, libraries, or simulated environments.
Include concrete experience with cloud platforms and containerization, specifying how you used them to manage computing resources for ML experiments.
Showcase any contributions to open-source ML tools, libraries, or documentation of best practices, as this aligns with the role's emphasis on maintaining workflows and enabling others.
✉️ What to Emphasize in Your Cover Letter
["Explain your interest in AI safety and how your background aligns with LawZero's specific focus on novel safety agendas for large models.", 'Provide a concise example of a challenging ML workflow you designed or optimized, highlighting your impact on research acceleration or resource efficiency.', 'Describe your experience collaborating with research teams, emphasizing how you facilitated the implementation of novel models or environments.', 'Mention your familiarity with the tech stack (e.g., PyTorch/TensorFlow/JAX, cloud platforms, Docker/Kubernetes) and how it applies to large-scale ML projects.']
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Investigate LawZero's public statements, blog posts, or research outputs related to AI safety to understand their specific agenda and technical focus.
- → Look into the backgrounds of their team members (if available on LinkedIn or company site) to identify potential research interests or project areas.
- → Review any publications or talks by the company to grasp their approach to large model training, inference, or safety methodologies.
- → Explore the broader AI safety ecosystem to contextualize LawZero's work and identify how your skills might address their unique challenges.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Submitting a generic application without tailoring it to AI safety or large-scale ML workflows—this role requires specificity.
- Overemphasizing theoretical ML knowledge without demonstrating hands-on experience with scalable implementations and tooling.
- Failing to provide examples of collaboration with researchers or contributions to team best practices, as this is core to the role's responsibilities.
📅 Application Timeline
This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.
Typical hiring timeline:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!