Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Applied AI/ML Senior Associate - Small Business
New York, NY, United States
Actively hiring, first seen by us 1 day ago- Pay
- Pay not listedSimilar roles state $166K–$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 postingBuild and own prompts, tool usage, memory, and routing for agent behavior
Build evaluations (golden and adversarial test sets, LLM-judge rubrics)
Implement and tune safety guardrails, including PII handling and spend limits
Fine-tune SLMs and open-weight models for custom objectives
Apply MLOps practices such as CI/CD, versioning, and observability
From the employer’s posting
you'll build and own the prompts, tool usage, memory, and routing that shape agent behavior
Evaluate agent performance with golden and adversarial test sets, LLM-judge rubrics, and evaluation libraries.
Implement and tune safety guardrails, including input/output checks, prompt-injection defenses, PII handling, and spend limits
Fine-tune SLMs and open-weight models for custom objectives.
Apply MLOps practices such as CI/CD, versioning, observability, and model and prompt lifecycle management.
Education & alternatives
Required Qualifications, Capabilities, and Skills - Bacholer’s, Master's, or PhD degree in computer science or a data science-related discipline, with 3+ years of industry experience - Strong software engineering experience contributing to production systems.
Posting record
Report an errorActively hiringFirst seen by us 1 day ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 3, 20261 day ago
- Recorded sightings
- 2
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 1
Early. Similar jobs usually come down between day 6 and day 69.
At JPMorgan Chase, jobs usually come down by about day 15.
From 4,418 similar postings that came down, and 914 of JPMorgan Chase’s. Coming down is not always a hire.
JPMorgan Chase is hiring
410 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring at a steady pace
- Takes jobs down at a usual pace
- 2% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- FastAPI
- Flask
- PyTorch
- scikit-learn
Where the posting names them
- Strong software engineering experience contributing to production systems. - Proficient in Python, including asynchronous programming and API development with FastAPI, Flask, or similar frameworks. - Hands-on experience building and deploying generative AI or LLM applications in production.
- Experience using tracing observability tools such as Arize Phoenix. - Familiarity with machine learning frameworks such as PyTorch or scikit-learn, including adapting and fine-tuning of models. - Experience integrating vector databases and search or indexing systems into AI applications.
Your next step
Complete your application on jpmc.fa.oraclecloud.com. The employer’s form will show what is required.
Applying through Oracle Recruiting
- Applying starts by asking for your email address.
- Each employer sets which files it accepts, so read the note beside the upload field.
Already applied? Track this application
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
Chase Small Business Banking is a world-class brand serving 7 million small business clients with a full suite of financial services, including lending, deposits, cards, and online solutions.
As a Senior Associate Applied AI/ML Scientist, you will develop and operate agentic AI products supporting Small Business Lending. You will partner with Product and Engineering to translate business needs into production-ready systems - you'll build and own the prompts, tool usage, memory, and routing that shape agent behavior, and build evaluations that show whether each change improves task success, safety, and cost. You will help maintain the continuous learning loop from production traces to failure analysis, evaluation cases, and verified fixes.
Job Responsibilities
- Design, build, and deploy machine learning and generative AI solutions to address business needs.
- Build agentic AI systems with LLM orchestration frameworks such as LangChain, LangGraph, or similar tools.
- Evaluate agent performance with golden and adversarial test sets, LLM-judge rubrics, and evaluation libraries.
- Fine-tune SLMs and open-weight models for custom objectives.
- Implement and tune safety guardrails, including input/output checks, prompt-injection defenses, PII handling, and spend limits; monitor effectiveness and false-positive impact.
- Partner with data scientists, product teams, and business stakeholders to turn requirements into scalable AI/ML solutions.
- Apply MLOps practices such as CI/CD, versioning, observability, and model and prompt lifecycle management.
- Work with structured and unstructured data, integrating relational and vector databases to support retrieval-augmented generation (RAG).
Present findings clearly to technical and non-technical stakeholders, including senior leaders.
Required Qualifications, Capabilities, and Skills
- Bacholer’s, Master's, or PhD degree in computer science or a data science-related discipline, with 3+ years of industry experience
- Strong software engineering experience contributing to production systems.
- Proficient in Python, including asynchronous programming and API development with FastAPI, Flask, or similar frameworks.
- Hands-on experience building and deploying generative AI or LLM applications in production.
- Hands-on experience with LLM orchestration and agentic AI frameworks such as LangChain, LangGraph, or similar tools.
- Experience implementing evaluations and guardrails using tools including deepeval and similar libraries.
- Experience using tracing observability tools such as Arize Phoenix.
- Familiarity with machine learning frameworks such as PyTorch or scikit-learn, including adapting and fine-tuning of models.
- Experience integrating vector databases and search or indexing systems into AI applications.
- Working knowledge of MLOps, including CI/CD, monitoring, and lifecycle management.
- Strong problem-solving and communication skills, with the ability to work with technical and non-technical stakeholders.
Source details
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
New York, NY, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.