Machine Learning Engineer

10a Labs•Washington, DC

12d•$130,000 - $200,000•Remote

About The Position

10a Labs is seeking a Machine Learning Engineer with 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. The engineer will work both independently and collaboratively on 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. The role involves collaborating closely with researchers, software engineers, red teamers, and subject-matter experts to develop production-ready systems that support leading AI organizations and technology companies.

Requirements

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) or 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.

Nice To Haves

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.

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.