How to apply for Machine Learning Engineer

About 10a Labs

10a Labs provides adversarial red teaming, model evaluations, and intelligence collection to companies deploying AI systems. Its clients include frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Working here means helping engineering, safety, and security teams stay ahead of evolving threats.

About the role

This machine learning engineer role focuses on designing, building, and evaluating ML systems for AI safety and model evaluation. You will run experiments on reinforcement learning, language models, multimodal systems, and classifiers. The work supports red teamers, analysts, and subject-matter experts who serve leading AI organizations.

A typical day

A typical day might involve writing code to train or fine-tune a model, running evaluation experiments, and analyzing results with red teamers or analysts. You could also spend time building or improving evaluation pipelines, datasets, and metrics. Collaboration with engineers and subject-matter experts on ambiguous technical questions is a core part of the work.

Who 10a Labs is looking for

• Strong ML engineering skills with hands-on experience training, fine-tuning, and evaluating models.
• Experience with reinforcement learning, NLP/LLMs, computer vision, or multimodal ML.
• Comfortable turning ambiguous technical questions into rigorous experiments and scalable pipelines.
• Able to build evaluation benchmarks, datasets, metrics, and reliable tooling for ML experiments.

Tips for this application

• Highlight any work you have done on model evaluations, red teaming, or adversarial testing, even if it was a side project or internal tool.
• List specific ML frameworks and tools you have used for training, fine-tuning, and evaluation, and mention the scale of models you worked with.
• Show that you can design experiments from scratch. Describe a time you took an open-ended question and turned it into a testable ML experiment.
• Mention any experience with safety, security, or robustness evaluations for AI systems, since 10a Labs focuses on these areas.
• If you have worked with multimodal or reinforcement learning systems, call that out clearly. The role spans RL, NLP/LLMs, computer vision, and multimodal ML.

What to cover in your cover letter

Explain why you want to work on AI safety and model evaluations specifically, not just ML in general. Describe a project where you built an evaluation pipeline or benchmark. Show you can collaborate with red teamers, analysts, and subject-matter experts. Mention any experience with adversarial testing or threat intelligence.

Research before applying

• Read the 10a Labs website, especially any pages about adversarial red teaming, model evaluations, and intelligence collection.
• Look up public work or talks by 10a Labs staff on AI safety, red teaming, or model evaluation.
• Understand what frontier AI labs and Fortune 10 companies typically need from safety and threat-intelligence vendors.
• Check recent news about AI safety evaluations and adversarial testing to see how 10a Labs fits into the broader landscape.

Likely interview topics

Based on the job description, expect questions about:

• How you would design an evaluation pipeline for a frontier language model.
• Your experience with reinforcement learning and how you would apply it to model evaluation.
• A time you turned an ambiguous ML problem into a concrete experiment and what you learned.
• How you would evaluate the robustness of a multimodal system against adversarial inputs.
• Your approach to building datasets and metrics for safety and security evaluations.

Common mistakes to avoid

• Applying with a generic ML resume that does not mention evaluations, red teaming, or safety work.
• Claiming experience with reinforcement learning or multimodal systems without concrete examples.
• Focusing only on model building and ignoring the experimental, evaluation-heavy nature of this role.