AI Summary
Seeking a Machine Learning Engineer with 3-5+ years of experience to design, build, and deploy advanced ML systems for AI safety, security, and intelligence applications. This role involves the full ML lifecycle, from data to deployment, focusing on multimodal systems, frontier model evaluations, and agentic workflows. Requires strong engineering fundamentals, a research mindset, and experience with Python, PyTorch/TensorFlow, and cloud platforms.
Key Highlights
Full ML lifecycle ownership from research to production deployment.
Focus on AI safety, security, and threat intelligence applications.
Work with frontier AI models and multimodal systems.
Key Responsibilities
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, and large-scale experimentation.
Own ML projects from initial research and prototyping through production deployment and monitoring.
Partner with software engineers to productionize ML systems and support ongoing improvements.
Provide technical expertise and guidance across client engagements and internal research initiatives.
Technical Skills Required
Benefits & Perks
Salary Range: $130K–$200K
Performance-based annual bonus
Support for conferences, continuing education, or leadership training
Fully remote, U.S.-based
Comprehensive health, dental, and vision coverage
Generous PTO and paid holiday schedule
Nice to Have
Familiarity or experience with model distillation, synthetic data generation, reinforcement learning, or AI evaluation research is strongly preferred.
Experience working with frontier language models, multimodal foundation models, or AI safety evaluations.
Prior experience in cybersecurity, trust and safety, abuse prevention, threat intelligence, or related domains.
Experience with retrieval-augmented generation (RAG), AI agent frameworks, and context orchestration systems such as LangChain, LlamaIndex, OpenAI Agents, or AutoGen.
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, and large-scale experimentation.
- Own ML projects from initial research and prototyping through production deployment and monitoring.
- Partner with software engineers to productionize ML systems and support ongoing improvements.
- Provide technical expertise and guidance across client engagements and internal research initiatives.
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- Brings curiosity, creativity, and rigor to ambiguous research and engineering problems, with a bias toward experimentation and rapid iteration;
- Thrives in collaborative, interdisciplinary environments while also being comfortable independently driving projects to completion;
- Communicates technical concepts clearly to both technical and non-technical audiences;
- Is resourceful, proactive, and comfortable operating in a fast-moving startup environment.
- Is excited about developing novel approaches that advance the state of AI safety, evaluation, and security.
- 3–5+ years of professional experience building and deploying machine learning systems.
- Strong proficiency in Python and modern machine learning frameworks such as PyTorch and/or TensorFlow
- Experience working across multiple modalities, with expertise in one or more of:
- Computer Vision: image classification, object detection, OCR, segmentation, deepfake detection, multimodal vision-language systems, or related areas.
- Natural Language Processing: LLMs, text classification, information extraction, retrieval systems, speech-to-text, agentic applications, or related areas.
- Experience training, fine-tuning, evaluating, and deploying machine learning models in production environments.
- Experience designing evaluation methodologies, benchmarking systems, and model performance metrics.
- Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.)
- Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ML infrastructure, workflow orchestration, storage, and database services.
- Familiarity or experience with model distillation, synthetic data generation, reinforcement learning, or AI evaluation research is strongly preferred.
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- Experience working with frontier language models, multimodal foundation models, or AI safety evaluations.
- Prior experience in cybersecurity, trust and safety, abuse prevention, threat intelligence, or related domains.
- Experience with retrieval-augmented generation (RAG), AI agent frameworks, and context orchestration systems such as LangChain, LlamaIndex, OpenAI Agents, or AutoGen.
- Salary Range: $130K–$200K, depending on experience and location
- Bonus: Performance-based annual bonus
- Professional Development: Support for conferences, continuing education, or leadership training
- Work Environment: Fully remote, U.S.-based
- Health Benefits: Comprehensive health, dental, and vision coverage
- Time Off: Generous PTO and paid holiday schedule
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