10a Labs
MachineLearningEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Machine Learning Engineer at 10a Labs. Skills: Machine learning, ML systems, Model evaluation, AI safety. Design machine learning models. Train machine learning models”
Industry & Context.
Ambiguous problems; High-impact problems
What They're Looking For.
Must Have
3-5+ years experience, Python proficiency, ML frameworks proficiency, Experience training models, Experience fine-tuning models, Experience evaluating models, Experience deploying models, Experience designing evaluation methodologies, Experience with benchmarking systems, Experience with model performance metrics, Experience with MLOps tools, Experience with cloud platforms
Nice to Have
GCP experience, Familiarity with model distillation, Familiarity with synthetic data generation, Familiarity with reinforcement learning, Familiarity with AI evaluation research, Experience with frontier language models, Experience with multimodal foundation models, Experience with AI safety evaluations, Experience in cybersecurity, Experience in trust and safety, Experience in abuse prevention, Experience in threat intelligence, Experience with RAG, Experience with AI agent frameworks, Experience with context orchestration systems
What You'll Do.
Design machine learning models
Train machine learning models
Evaluate machine learning models
Deploy machine learning models
Develop classification systems
Improve classification systems
Contribute to model distillation
Contribute to model optimization
Contribute to model fine-tuning
Design evaluation pipelines
Design testing frameworks
Build agentic systems
Build automated workflows
Prototype ML projects
Partner with software engineers
Productionize ML systems
Support ongoing improvements
Provide technical expertise
How You'll Work.
Team & Collaboration
Collaborate with researchers; Collaborate with software engineers; Collaborate with red teamers; Collaborate with subject-matter experts; Interdisciplinary environments
Communication Scope
Communicate technical concepts
Process & Methodology
ML project ownership
Full Job Description
About 10a Labs: 10a Labs is the safety and threat-intelligence layer trusted by frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Our adversarial red teaming, model evaluations, and intelligence collection enable engineering, safety, and security teams to stay ahead of evolving threats and deploy AI systems safely. About the Role: We are seeking a Machine Learning Engineer (3–5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and intelligence applications. This role spans the full ML lifecycle, from dataset development and experimentation to model training, evaluation, deployment, and monitoring. You will work both independently and collaboratively across projects involving multimodal classification systems, frontier model evaluations, model distillation research, and agentic workflows. The ideal candidate combines strong engineering fundamentals with a research mindset and enjoys tackling ambiguous, high-impact problems at the frontier of AI. You will collaborate closely with researchers, software engineers, red teamers, and subject-matter experts to develop production-ready systems that support leading AI organizations and technology companies. Responsibilities may include: Design, train, evaluate, and deploy machine learning 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, evaluate, and compare AI models, including large language models and multimodal systems. Contribute to model distillation, optimization, and fine-tuning efforts to improve performance, efficiency, and deployability. Design evaluation pipelines, metrics, and testing frameworks to measure model capabilities, reliability, and safety. Build agentic systems and automated workflows for evaluation, red teaming, research
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