10a Labs
Machine Learning Engineer
About this role
10a Labs seeks an experienced Machine Learning Engineer to design, build, and deploy ML systems for AI safety, security, and threat intelligence applications. You'll work across the full ML lifecycle—from dataset development through production deployment—on projects involving multimodal classification, model evaluation, and agentic workflows, collaborating with researchers and engineers to support leading AI organizations.
What you'll do
- Design, train, evaluate, and deploy ML models across text, image, audio, and multimodal domains
- Develop and improve classification systems for safety, security, abuse detection, and intelligence applications
- Conduct experiments to benchmark and compare AI models, including large language models and multimodal systems
- Contribute to model distillation, optimization, and fine-tuning efforts to improve deployability
- Design evaluation pipelines, metrics, and testing frameworks to measure model capabilities and safety
- Build agentic systems and automated workflows for evaluation, red teaming, and large-scale experimentation
What they're looking for
- Python and modern ML frameworks (PyTorch, TensorFlow)
- Computer Vision (image classification, object detection, multimodal vision-language systems)
- Natural Language Processing (LLMs, text classification, agentic applications)
- Model training, fine-tuning, evaluation, and production deployment
- MLOps tools and practices (Docker, Kubernetes, CI/CD, MLflow)
- Cloud platforms (GCP preferred, AWS, or Azure)
- Model distillation, synthetic data generation, or reinforcement learning
- Frontier language models and AI safety evaluations
Benefits
- Salary range: $130K–$200K depending on experience and location
- Performance-based annual bonus
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10a Labs
10a Labs builds AI safety and threat intelligence solutions that help organizations detect and mitigate risks in AI systems. The company is hiring backend engineers, machine learning engineers, and data engineers to develop scalable APIs, ML systems, and data pipelines that power these safety-focused applications.
View all jobs at 10a LabsLikely interview questions
- Walk us through a machine learning project where you trained, evaluated, and deployed a model end-to-end. What metrics did you use to assess performance, and how did you handle the transition from experimentation to production?
- Describe your experience with multimodal systems or working across different data modalities (text, image, audio). Which have you worked with most, and what were the key challenges?