JPMorganChase
2026 Machine Learning Center of Excellence (NLP)-Internship
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2026 Machine Learning Center of Excellence (NLP)-Internship
Machine Learning Internship – Summer Associate Internship Program
Machine Learning Center of Excellence (MLCOE), Chief Data & Analytics Office (CDAO) @ JPMorgan Chase & Co.
The Chief Data & Analytics Office (CDAO) at JPMorgan Chase drives the firm’s data and analytics transformation by fostering innovation in machine learning (ML) and artificial intelligence (AI). The Machine Learning Center of Excellence (MLCOE) partners across the business to develop and deploy ML solutions that address critical challenges in risk management, product innovation, and operational efficiency. This internship provides hands-on experience in cutting-edge natural language processing (NLP), large language models, speech recognition, reinforcement learning, and recommendation systems—enabling impactful contributions to global financial services.
Overview
This 12-week summer internship (flexible start in June, aligning with academic calendars) offers direct exposure to enterprise-scale ML, with the opportunity for long-term engagement leading to conversion into full-time employment. As an intern, you’ll:
- Work alongside senior ML engineers, data scientists, and domain specialists to solve real-world business problems
- Translate research into production-ready models with measurable business impact
- Collaborate with cross-functional teams (e.g., Product, Compliance, Business Strategy) to embed technology responsibly
- Present findings in a speaker series with JPMorgan’s leadership
Why Join?
- Contribute to published research or internal innovation pipelines with potential presentation opportunities at top AI/ML conferences
- Apply rigorous data science and deep learning techniques in a high-trust financial services environment
- Join a hybrid work model with flexible structures tailored to remote collaboration and office-based deep work
- Gain mentorship from experts in reinforcement learning, NLP, and enterprise deployment strategies
Key Responsibilities
Core Tasks
- Innovate: Research and prototype next-gen ML techniques, with a focus on:
- Natural Language Processing (NLP) and large language models (LLMs)
- Speech recognition/understanding and real-time analytics
- Reinforcement learning for decision-making pipelines
- Recommendation systems for personalized insights
- Develop: Build production-grade models using state-of-the-art frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers) while addressing bugs, performance bottlenecks, and scaling challenges
- Collaborate: Partner with business units, legal/compliance teams, and technologists to align ML solutions with regulatory requirements and commercial objectives
- Communicate: Articulate technical findings clearly to both technical and non-technical stakeholders, from slide decks to whitepapers
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Supporting Engagement
- Attend internal and external conferences (e.g., scholarly workshops, industry forums) to stay ahead of advancements
- Join knowledge-sharing sessions within MLCOE, including deep dives into model explainability, robustness, and fairness
- Support data curation initiatives to enhance data quality, security, and accessibility for training pipelines
Requirements
Must-Have Qualifications
- Academic eligibility:
- Enrolled in a PhD or MS program in:
- Computer Science (with demonstration of ML specialisation), Electrical Engineering, Mathematics, Operations Research, Optimization, Data Science, or related fields
- Expected graduation: December 2026 through August 2027
- Enrolled in a PhD or MS program in:
- Technical expertise:
- Proficient in Python with hands-on experience in deep learning frameworks (PyTorch, TensorFlow, or similar)
- Demonstrated capability in statistical/ML toolkits (NumPy, Scikit-Learn, Pandas, PyTorch Lightning)
- Deep understanding of core ML paradigms (e.g., attention mechanisms, neural architecture search, generative models)
- Experience with experimental design, A/B testing, and measuring model performance (intrinsic metrics like AUC, extrinsic ROI)
- Transferable skills:
- Strong problem-solving coupled with a passion for scalable machine learning applied to business problems
- Ability to ** Робисокritique existing solutions** and propose novel approaches
- Presentable written/verbal communication for stakeholder briefings
- Comfort working in high-pressure, interdisciplinary teams with rapid delivery expectations
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Nice-to-Have (Preferred)
- Domain knowledge:
- Prior familiarity with financial services (e.g., algortithmic trading, credit risk modeling)
- Insights into LLMs, prompt engineering, or multimodal systems
- Researcch impact:
- At least one peer-reviewed paper or conference submission (e.g., NeurIPS, ICML, ACL) in NLP, deep learning, reinforcement learning, or speech tech
- Familiarity with end-to-end ML engineering, including CI/CD pipelines (Jenkins), unit testing (pytest), and Cloud deployment (AWS/GCP)
- Exposure to automated mechanistic explanations (e.g., LIME, SHAP) or ethical considerations for high-responsibility AI systems
Benefits
- Mentorship: Pair with leadership to navigate challenges, from academic alignment to real-world problem formulation
- Networking: Access to workshops with executive AI leaders, as well as peer metering circles
- Impact: See projects live in production systems, influencing products like flow automation, fraud detection, or investmenstrative decision engines.
- Growth: Extension into full-time roles based on performance and mutual fit within teams like ML engineering, data platform architecture, or Head of AI research
- Work-life balance: Design a schedule that leverages asynchronous collaboration, JPMorgan’s hybrid slate options, and token-based accrual for flexibility
How to Apply
🔗 More information: https://www.jpmorgan.com/mlcoe
Interested candidates are invited to submit a research problem statement or GitHub repository alongside their application to differentiate conceptual and technical depth.
Key Links
#MLCOE_jobs | JPMorgan Career Portal: Jobs at JPMorgan
JPMorgan Chase Commitments: We champion diversity, accessibility, accessibility adaptations, and are among the first APAC companies to join Science Based Targets initiatives. We incentivise thinking differently—represented by our 2023 SEC Award for innovation in zero-trust AI governance.
Note: Preference given to applications submitted before spring 2025 round closes.
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