Applied AI and Machine Learning Lead
Jersey City, NJ, United States
- Pay
- Pay not listedSimilar roles state $165K–$240K
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingLead generative and agentic AI initiatives from experimentation through production release
Design and evaluate foundation-model approaches, including fine-tuning and safety controls
Establish evaluation practices covering quality, robustness, latency, cost, and risk
Build and operate production-grade ML and generative AI services with monitoring
Mentor and develop engineers and data scientists through reviews and coaching
From the employer’s posting
Lead end-to-end generative AI and agentic AI initiatives from problem framing and experimentation through production release and measurable adoption.
Design and evaluate foundation-model approaches, including model selection, fine-tuning strategies, retrieval and context design, and safety controls.
Establish rigorous evaluation practices (quality, robustness, latency, cost, and risk) and translate results into clear recommendations and roadmaps.
Build and operationalize production-grade machine learning and generative AI services using sound engineering practices, monitoring, and incident-ready controls.
Mentor and develop engineers and data scientists through technical leadership, reviews, and coaching to raise team capability and delivery velocity.
Education & alternatives
Required Qualifications, Capabilities, and Skills - Master’s degree in computer science, engineering, statistics, or a related quantitative discipline. - 7+ years of applied experience building and deploying machine learning and generative AI solutions in production environments.
Preferred Qualifications, Capabilities, and Skills - PhD in a relevant quantitative field or demonstrated applied research impact in machine learning or generative AI. - Publications, patents, or open-source contributions that demonstrate research depth and practical impact.
Tools in this posting
- Python
- PyTorch
Source — Tool mentions in context
- 7+ years of applied experience building and deploying machine learning and generative AI solutions in production environments. - Strong proficiency in Python and modern machine learning frameworks (for example, PyTorch). - Demonstrated ability to lead experimentation and evaluation, including defining metrics, running controlled comparisons, and documenting results for stakeholders.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 15, 2026 · 14 days ago
- Recorded sightings
- 107
- Last seen by us
- Oct 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on jpmc.fa.oraclecloud.com. The employer’s form will show what is required.
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Job description
As an Applied AI and Machine Learning Lead at JPMorganChase within the Data Science and AI team supporting our Technology organization, you will drive high-impact generative AI and advanced analytics initiatives from research through production. You will lead the design, evaluation, and deployment of foundation-model and agentic AI capabilities that improve decision-making, automate work, and raise engineering standards for reliability and governance. You will partner with product, engineering, and risk stakeholders to define measurable outcomes and deliver solutions that are scalable, robust, and responsibly deployed.
Job Responsibilities
- Lead end-to-end generative AI and agentic AI initiatives from problem framing and experimentation through production release and measurable adoption.
- Design and evaluate foundation-model approaches, including model selection, fine-tuning strategies, retrieval and context design, and safety controls.
- Establish rigorous evaluation practices (quality, robustness, latency, cost, and risk) and translate results into clear recommendations and roadmaps.
- Build and operationalize production-grade machine learning and generative AI services using sound engineering practices, monitoring, and incident-ready controls.
- Define and drive semantic consistency across systems by leading semantic modeling standards and lifecycle governance in partnership with domain experts.
- Implement integration patterns that align data pipelines, application interfaces, and user experiences to reduce semantic conflicts and increase trust in outcomes.
- Partner with cross-functional leaders to define success metrics, manage trade-offs, and deliver outcomes aligned to business priorities and oversight expectations.
- Mentor and develop engineers and data scientists through technical leadership, reviews, and coaching to raise team capability and delivery velocity.
- Communicate complex technical concepts to both technical and non-technical audiences, influencing decisions through clarity, evidence, and pragmatism.
Required Qualifications, Capabilities, and Skills
- Master’s degree in computer science, engineering, statistics, or a related quantitative discipline.
- 7+ years of applied experience building and deploying machine learning and generative AI solutions in production environments.
- Strong proficiency in Python and modern machine learning frameworks (for example, PyTorch).
- Demonstrated ability to lead experimentation and evaluation, including defining metrics, running controlled comparisons, and documenting results for stakeholders.
- Experience deploying and operating models in production, including performance optimization, monitoring, and reliability trade-offs.
- Working knowledge of responsible AI practices, model risk concepts, and governance controls suitable for regulated environments.
- Strong software engineering fundamentals, including version control, code review, testing, and maintainable design.
- Proven ability to partner across product, engineering, and business stakeholders to translate ambiguous needs into clear outcomes and delivery plans.
Preferred Qualifications, Capabilities, and Skills
- PhD in a relevant quantitative field or demonstrated applied research impact in machine learning or generative AI.
- Publications, patents, or open-source contributions that demonstrate research depth and practical impact.
- Hands-on experience with transformer-based architectures, large-scale training or fine-tuning, and GPU-enabled development workflows.
- Experience building agentic AI systems (tool use, orchestration, planning, and workflow automation) with strong evaluation discipline.
- Familiarity with semantic modeling, ontologies, or semantic-layer design to improve consistency across analytics and AI use cases.
- Experience applying AI solutions within financial services or other highly regulated industries.
Source details
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- Pay
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- Location & working pattern
Jersey City, NJ, United States
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