Machine Learning Fellowship
10a Labs
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New York, NY
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Mid-Senior level
Posted March 14, 2026
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Responsibilities
Commitments
Responsibilities
- Assist in the development and maintenance of bespoke classification systems, contributing across the project lifecycle.
- Collaborate with engineers across disciplines to co-design project timelines for model development, gaining hands-on experience in planning data pipelines, building infrastructure, deploying models, and assessing performance/latency metrics.
- Research new approaches to help streamline existing processes with the help of machine learning algorithms.
- Assist with research experiment design and automation, particularly as it relates to abuse detection or red teaming of AI systems.
- Ideate / brainstorm new research approaches to known and novel problems in the Trust & Safety and AI Security fields.
- Brings curiosity and creativity to ambiguous research problems, with a bias toward experimentation and rapid iteration.
Commitments
Flexible start / end dates
Remote work (based in the continental U.S.)
Flexible schedule, up to 20 hours per week (negotiable)
Not Met Priorities
What still needs stronger evidence
Requirements
- Strong academic background and quantitative foundation demonstrated through applied machine learning coursework, research, or hands-on-experience.
- Experience with NLP foundations and text data processing, including cleaning, tokenization, and feature engineering for downstream model development.
- Strong Python background with practical experience using multiple ML frameworks (PyTorch, TensorFlow, scikit-learn, etc.) to prototype, train, and evaluate models in real-world applications.
- Practical experience in generative model adaptation through fine-tuning, prompt-engineering, and in-context learning on low-resource or specialized datasets
- Clear communicator of technical concepts for non-technical audiences.
- Strong understanding of modeling concepts including bias-variance tradeoffs, regularization, generalization, data imbalance, and model calibration to design models in challenging problem spaces.
Preferred Skills
- Computer vision skills (OCR, image classification, deep fake detection).
- Experience working in cloud environments such as AWS or GCP for end-to-end ML workflows, including model training, deployment, and monitoring using tools like Vertex AI, SageMaker, and cloud-native ML libraries.
- Familiarity with multimodal learning (text-image or text-audio) or cross-domain model evaluation.
- Exposure to MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.).
- Experience managing full lifecycle machine learning projects from design to deployment.
- Understanding of modern retrieval-augmented generation (RAG), AI agent frameworks, and context-aware orchestration (e.g., LangChain, LlamaIndex, OpenAI Agents, or AutoGen) for building intelligent applications.
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 : As a Machine Learning Fellow, you will apply machine learning techniques to high-impact research problems. Fellows will contribute across the ML lifecycle — from curating and processing diverse data sources, to developing, fine-tuning, and evaluating models. This is a hands-on role at the intersection of applied research and practical ML development, with opportunities to explore novel methods, test ideas quickly, and generate insights.
In This Role, You Will
Assist in the development and maintenance of bespoke classification systems, contributing across the project lifecycle.
Collaborate with engineers across disciplines to co-design project timelines for model development, gaining hands-on experience in planning data pipelines, building infrastructure, deploying models, and assessing performance/latency metrics.
Research new approaches to help streamline existing processes with the help of machine learning algorithms.
Assist with research experiment design and automation, particularly as it relates to abuse detection or red teaming of AI systems.
Ideate / brainstorm new research approaches to known and novel problems in the Trust & Safety and AI Security fields.
We’re Looking For Someone Who
Brings curiosity and creativity to ambiguous research problems, with a bias toward experimentation and rapid iteration.
Thrives in collaborative, interdisciplinary environments; is resourceful, proactive, and adaptable.
Is comfortable communicating technical ideas clearly to both technical and non-technical audiences.
Is excited about contributing to real-world applications of ML and exploring new methods that push beyond standard benchmarks.
Requirements
Strong academic background and quantitative foundation demonstrated through applied machine learning coursework, research, or hands-on-experience.
Experience with NLP foundations and text data processing, including cleaning, tokenization, and feature engineering for downstream model development.
Strong Python background with practical experience using multiple ML frameworks (PyTorch, TensorFlow, scikit-learn, etc.) to prototype, train, and evaluate models in real-world applications.
Practical experience in generative model adaptation through fine-tuning, prompt-engineering, and in-context learning on low-resource or specialized datasets
Clear communicator of technical concepts for non-technical audiences.
Strong understanding of modeling concepts including bias-variance tradeoffs, regularization, generalization, data imbalance, and model calibration to design models in challenging problem spaces.
Nice To Have
Computer vision skills (OCR, image classification, deep fake detection).
Experience working in cloud environments such as AWS or GCP for end-to-end ML workflows, including model training, deployment, and monitoring using tools like Vertex AI, SageMaker, and cloud-native ML libraries.
Familiarity with multimodal learning (text-image or text-audio) or cross-domain model evaluation.
Exposure to MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.).
Experience managing full lifecycle machine learning projects from design to deployment.
Understanding of modern retrieval-augmented generation (RAG), AI agent frameworks, and context-aware orchestration (e.g., LangChain, LlamaIndex, OpenAI Agents, or AutoGen) for building intelligent applications.
Benefits
Flexible start / end dates
Remote work (based in the continental U.S.)
Flexible schedule, up to 20 hours per week (negotiable)
Hourly pay commensurate with experience and qualifications
$30 per hour for undergraduate students
$35 per hour for graduate students
$50 per hour for advanced PhD students
$60 per hour for postdocs or non-tenured positions
$125 per hour for tenure-track academics
About the role : As a Machine Learning Fellow, you will apply machine learning techniques to high-impact research problems. Fellows will contribute across the ML lifecycle — from curating and processing diverse data sources, to developing, fine-tuning, and evaluating models. This is a hands-on role at the intersection of applied research and practical ML development, with opportunities to explore novel methods, test ideas quickly, and generate insights.
In This Role, You Will
Assist in the development and maintenance of bespoke classification systems, contributing across the project lifecycle.
Collaborate with engineers across disciplines to co-design project timelines for model development, gaining hands-on experience in planning data pipelines, building infrastructure, deploying models, and assessing performance/latency metrics.
Research new approaches to help streamline existing processes with the help of machine learning algorithms.
Assist with research experiment design and automation, particularly as it relates to abuse detection or red teaming of AI systems.
Ideate / brainstorm new research approaches to known and novel problems in the Trust & Safety and AI Security fields.
We’re Looking For Someone Who
Brings curiosity and creativity to ambiguous research problems, with a bias toward experimentation and rapid iteration.
Thrives in collaborative, interdisciplinary environments; is resourceful, proactive, and adaptable.
Is comfortable communicating technical ideas clearly to both technical and non-technical audiences.
Is excited about contributing to real-world applications of ML and exploring new methods that push beyond standard benchmarks.
Requirements
Strong academic background and quantitative foundation demonstrated through applied machine learning coursework, research, or hands-on-experience.
Experience with NLP foundations and text data processing, including cleaning, tokenization, and feature engineering for downstream model development.
Strong Python background with practical experience using multiple ML frameworks (PyTorch, TensorFlow, scikit-learn, etc.) to prototype, train, and evaluate models in real-world applications.
Practical experience in generative model adaptation through fine-tuning, prompt-engineering, and in-context learning on low-resource or specialized datasets
Clear communicator of technical concepts for non-technical audiences.
Strong understanding of modeling concepts including bias-variance tradeoffs, regularization, generalization, data imbalance, and model calibration to design models in challenging problem spaces.
Nice To Have
Computer vision skills (OCR, image classification, deep fake detection).
Experience working in cloud environments such as AWS or GCP for end-to-end ML workflows, including model training, deployment, and monitoring using tools like Vertex AI, SageMaker, and cloud-native ML libraries.
Familiarity with multimodal learning (text-image or text-audio) or cross-domain model evaluation.
Exposure to MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.).
Experience managing full lifecycle machine learning projects from design to deployment.
Understanding of modern retrieval-augmented generation (RAG), AI agent frameworks, and context-aware orchestration (e.g., LangChain, LlamaIndex, OpenAI Agents, or AutoGen) for building intelligent applications.
Benefits
Flexible start / end dates
Remote work (based in the continental U.S.)
Flexible schedule, up to 20 hours per week (negotiable)
Hourly pay commensurate with experience and qualifications
$30 per hour for undergraduate students
$35 per hour for graduate students
$50 per hour for advanced PhD students
$60 per hour for postdocs or non-tenured positions
$125 per hour for tenure-track academics