Lead Applied AI Research Scientist
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What you'll do
- Advance applied research across the LLM stack, including model training, adaptation, inference, and agentic systems.
- Design, build, and release foundation-model and agentic systems that support high-value business workflows from prototype to production.
- Develop methods to evaluate, verify, and monitor models and agents, including faithfulness, hallucination detection, and workflow verification.
- Enhance research capabilities in model explainability, reliability, and interpretability.
- Provide innovative research solutions to internal project teams.
- Collaborate with researchers to document findings in scientific papers and present at conferences.
- Contribute to intellectual property by pursuing necessary protections for generated IP.
- Work with cross-functional teams to integrate AI solutions into business processes.
- Present research outcomes to stakeholders and at industry conferences.
- Stay current with advancements in AI and machine learning.
- Foster a culture of innovation and continuous improvement within the team.
What they're looking for
- Ph.D. in computer science, machine learning, or related fields, or a Master’s degree with equivalent applied research and engineering experience.
- Ability to build and ship AI/ML systems, ideally involving LLMs.
- Proficiency in Python and modern ML frameworks such as PyTorch or JAX.
- Hands-on experience across the LLM lifecycle, including fine-tuning, prompting, serving, and evaluation.
- Experience in scientific technical writing.
- Strong communication skills and ability to present findings to a non-technical audience.
- Experience in model training and adaptation (e.g., supervised fine-tuning, RLHF, preference optimization, distillation, parameter-efficient methods).
- Experience in inference and serving (e.g., test-time compute, speculative decoding, quantization, KV-cache management).
- Experience in agentic systems (e.g., planning, tool and function calling, retrieval-augmented generation, orchestration).
- Experience in evaluation and faithfulness (e.g., benchmarks, LLM-as-judge, hallucination and groundedness detection, workflow verification).
- Experience in explainability and reliability (e.g., attribution and interpretability, uncertainty and calibration, monitoring for drift and failure).
Nice to have
- Strong record of impact through shipped AI systems or publications at top venues (such as NeurIPS, ICML, ICLR, ACL, EMNLP).
- Experience with foundation models, including tabular foundation models.
- No prior familiarity with finance or financial use cases required.
- Preference for candidates who include a link to their Google Scholar or Semantic Scholar profile in their resume.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Join us to push the boundaries of AI and contribute to groundbreaking discoveries in a collaborative, fast-paced environment. You’ll have the opportunity to advance your career by working with the Global Technology Applied Research (GTAR) center, where your expertise will help develop next-generation solutions for our clients and businesses. We value your creativity, technical skills, and passion for research, offering you a platform to make a real difference. At JPMorganChase, you’ll be part of a diverse team that values innovation and supports your growth. Your work will directly impact the firm’s technology and intellectual property.
As an Applied AI Research Scientist – Sr. Associate in the Global Technology Applied Research (GTAR) center, you will drive applied research across the LLM stack, including model training, inference, agentic systems, and evaluation methods. You will build and release practical AI systems for our businesses, provide innovative research solutions to internal teams, and contribute to the protection of intellectual property. You’ll collaborate with other researchers to document and present your findings, helping shape the future of AI at JPMorganChase. Your role will be central to advancing trustworthy and explainable AI systems within a supportive and innovative team culture.
Job Responsibilities:
- Advance applied research across the LLM stack, including model training, adaptation, inference, and agentic systems.
- Design, build, and release foundation-model and agentic systems that support high-value business workflows from prototype to production.
- Develop methods to evaluate, verify, and monitor models and agents, including faithfulness, hallucination detection, and workflow verification.
- Enhance research capabilities in model explainability, reliability, and interpretability.
- Provide innovative research solutions to internal project teams.
- Collaborate with researchers to document findings in scientific papers and present at conferences.
- Contribute to intellectual property by pursuing necessary protections for generated IP.
- Work with cross-functional teams to integrate AI solutions into business processes.
- Present research outcomes to stakeholders and at industry conferences.
- Stay current with advancements in AI and machine learning.
- Foster a culture of innovation and continuous improvement within the team.
Required Qualifications, Capabilities, and Skills:
- Ph.D. in computer science, machine learning, or related fields, plus at least 2 years of experience (industry or postdoc), or a Master’s degree with equivalent applied research and engineering experience plus 4 years of industry experience.
- Ability to build and ship AI/ML systems, ideally involving LLMs.
- Proficiency in Python and modern ML frameworks such as PyTorch or JAX.
- Hands-on experience across the LLM lifecycle, including fine-tuning, prompting, serving, and evaluation.
- Experience in scientific technical writing.
- Strong communication skills and ability to present findings to a non-technical audience.
- Experience in model training and adaptation (e.g., supervised fine-tuning, RLHF, preference optimization, distillation, parameter-efficient methods).
- Experience in inference and serving (e.g., test-time compute, speculative decoding, quantization, KV-cache management).
- Experience in agentic systems (e.g., planning, tool and function calling, retrieval-augmented generation, orchestration).
- Experience in evaluation and faithfulness (e.g., benchmarks, LLM-as-judge, hallucination and groundedness detection, workflow verification).
- Experience in explainability and reliability (e.g., attribution and interpretability, uncertainty and calibration, monitoring for drift and failure).
Preferred Qualifications, Capabilities, and Skills:
- Strong record of impact through shipped AI systems or publications at top venues (such as NeurIPS, ICML, ICLR, ACL, EMNLP).
- Experience with foundation models, including tabular foundation models.
- No prior familiarity with finance or financial use cases required.
Company
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