ZipRecruiter

Log In

1

Volunteer Large Language Model Llm Jobs in Rochester, NY

CaterTrax

Senior Agentic AI Engineer

Rochester, NY · On-site

$103K - $141K/yr

Large language model (LLM) platforms: OpenAI / Azure OpenAI, Anthropic Claude, AWS Bedrock, or equivalent. * Agent orchestration frameworks, function/tool calling, and multi-agent patterns.

Volaris Group

Senior Agentic AI Engineer

Rochester, NY · On-site

$103K - $141K/yr

Large language model (LLM) platforms: OpenAI / Azure OpenAI, Anthropic Claude, AWS Bedrock, or equivalent. * Agent orchestration frameworks, function/tool calling, and multi-agent patterns.

Pwc

AI Solutions Engineering Delivery Lead

Rochester, NY

$101K - $133K/yr

... Large Language Models (LLMs) and orchestrated applications - Manage and own the full lifecycle of ... LLM applications - Proven specialization in Python, Pandas, Scikit-learn, PyTorch, Langchain ...

Varsity Tutors

ChatGPT Tutor

Rochester, NY · Remote

$18 - $40/hr

... of large language model limitations. Ability to explain effective prompt construction, context window management, and responsible AI usage while preparing students for productive and ethical AI ...

Pwc

AI Engineer

Rochester, NY

$50K - $112K/yr

... LLM-as-judge pipelines, regression testing, and adversarial benchmarking, to assess reasoning, tool-calling reliability, and output groundedness - Optimizing open-weight language models, including ...

Pwc

ERP AI Engineer - Manager

Rochester, NY

$99K - $232K/yr

... with Large Language Models and prompt engineering - Building scalable, cloud-native microservices and containerized deployments - Proficiency with MLOps tooling and CI/CD pipelines for ML ...

next page

Showing results 1-20

All JobsVolunteer Large Language Model Llm JobsVolunteer Large Language Model Llm Jobs in Rochester, NY

Volunteer Large Language Model Llm information

See Rochester, NY salary details

$8

$18

$33

How much do volunteer large language model llm jobs pay per hour?

As of Aug 30, 2026, the average hourly pay for volunteer large language model llm in Rochester, NY is $18.88, according to ZipRecruiter salary data. Most workers in this role earn between $14.23 and $19.90 per hour, depending on experience, location, and employer.

What is a volunteer large language model LLM?

Volunteer Large Language Model (LLM) roles involve individuals contributing their time and expertise to support the development, testing, or improvement of large language models. Volunteers may help by annotating data, testing models for biases, providing feedback, or assisting with community moderation and outreach. This work is important for advancing the accuracy, fairness, and usefulness of language models, and often takes place within open-source or academic projects. Volunteers typically do not receive monetary compensation but gain experience and contribute to impactful technology.

What skills and qualifications are needed to thrive as a volunteer large language model LLM?

To thrive as a Volunteer Large Language Model LLM, you need a deep understanding of natural language processing, machine learning principles, and strong programming skills, typically supported by education in computer science or related fields. Familiarity with frameworks like TensorFlow or PyTorch, experience with large-scale data sets, and knowledge of cloud platforms are commonly required. Adaptability, collaboration, and effective communication are important soft skills for working in open-source or community-driven AI projects. These skills are crucial to developing, refining, and responsibly deploying advanced language models in dynamic and collaborative environments.

What are common challenges faced by volunteer large language model LLM contributors, and how can they be addressed?

Volunteer LLM contributors often encounter challenges such as coordinating with a distributed team, managing their time effectively alongside other commitments, and staying updated on rapidly evolving AI technologies. Collaboration tools like shared code repositories and communication platforms help streamline teamwork and reduce miscommunication. To address these challenges, it's helpful to set clear expectations, regularly participate in team meetings, and proactively seek feedback from experienced contributors. This approach not only fosters a supportive environment but also enhances your learning experience and impact.

What is the difference between Volunteer Large Language Model Llm vs Data Annotator?

AspectVolunteer Large Language Model LlmData Annotator
Required credentialsNone or basic technical knowledgeBasic computer skills, sometimes specific software training
Work environmentRemote or online, collaborativeOffice or remote, task-specific
Industry usageAI development, NLP projectsData labeling, machine learning training
Common search intentUnderstanding AI model training rolesData labeling and annotation roles

Volunteer Large Language Models (LLMs) are involved in training and improving AI language models, often through collaborative, volunteer efforts. Data Annotators focus on labeling data to train machine learning models. While both roles support AI development, LLM volunteers typically contribute to model training directly, whereas Data Annotators prepare data for such training.

What are popular job titles related to Volunteer Large Language Model Llm jobs in Rochester, NY?

For Volunteer Large Language Model Llm jobs in Rochester, NY, the most frequently searched job titles are:

What job categories do people searching Volunteer Large Language Model Llm jobs in Rochester, NY look for?

The top searched job categories for Volunteer Large Language Model Llm jobs in Rochester, NY are:

Volunteer Large Language Model Llm jobs near you

Senior Agentic AI Engineer

CaterTrax

Rochester, NY • On-site

$103K - $141K/yr

Full-time

This job post has expired 4 days ago. Applications are no longer accepted.

Job description

Hungry, humble and smart? If you have these qualities, we want you on the team.

Job Summary:

The Senior Agentic AI Engineer is the operational owner of the Constituent Data Layer (CDL) and the safe, secure rollout of AI agents across CaterTrax constituent surfaces.
Reporting to the Director, Enterprise Architecture as a member of the Data Layer group, this engineer manages the CDL schema, the agent registry, and the policy engine that governs which agents can access which data under which conditions, and instruments the data flywheel that returns outcomes from agent actions back into the layer.
The role partners with the Director of Strategic Solutions on Brief-to-agent translation, with the Director, Product and Solutions on engineering intake, with platform engineering on runtime, and with Finance on data flywheel ROI metrics. Without this role, agents would be built ad hoc across teams with no shared governance; this seat ensures they are not.

Job Description:

ESSENTIAL DUTIES AND RESPONSIBILITIES

CONSTITUENT DATA LAYER OWNERSHIP

  • Manage the Constituent Data Layer schema, including versioning, integrity, and evolution as constituent surfaces expand.
  • Own the agent registry: the authoritative record of every agent operating across CaterTrax surfaces.
  • Build and operate the policy engine governing which agents can access which data under which conditions.
  • Establish and maintain shared governance so agents are built on the CDL rather than ad hoc across teams.

AGENT DEVELOPMENT & SAFE ROLLOUT

  • Design, build, and deploy AI agents across constituent surfaces on the shared platform substrate.
  • Implement guardrails, evaluation harnesses, and monitoring for agent behavior in production.
  • Define and operate the rollout process for new agents, including staged release, human oversight, and rollback.
  • Lead incident response for agent behavior issues, including root-cause analysis and remediation.

DATA FLYWHEEL & MEASUREMENT

  • Instrument the data flywheel that returns outcomes from agent actions back into the Constituent Data Layer.
  • Partner with Finance to define and report data flywheel ROI metrics.
  • Ensure agent telemetry supports both product improvement and executive decision-making.

CROSS-FUNCTIONAL PARTNERSHIP

  • Partner with the Director of Strategic Solutions on Brief-to-agent translation, converting constituent problem framing into agent designs.
  • Partner with the Director, Product and Solutions on engineering intake and sequencing.
  • Partner with platform engineering on agent runtime, deployment, and operational readiness.

ENGINEERING EXCELLENCE

  • Uphold engineering standards, code review practices, and documentation quality across all Data Layer work.
  • Embed security and privacy by design into every agent and data-layer decision.
QUALIFICATIONS AND EXPERIENCE INCLUDES

EDUCATION

Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline required.

Master's degree preferred.

EXPERIENCE

Required:

  • 6+ years of professional software engineering experience, including 2+ years building LLM-based or AI-powered systems in production.
  • Hands-on experience with retrieval-augmented generation (RAG), vector databases, and prompt/agent design patterns.
  • Strong data modeling and API design skills, including schema governance in a shared-platform context.
  • Experience implementing evaluation, monitoring, and guardrails for AI systems in production.
  • Proficiency in Python and/or a modern backend language used in production services.

Preferred:

  • Experience with agent orchestration frameworks, agent registries, or policy/authorization engines.
  • Experience in foodservice technology, hospitality tech, or B2B SaaS platforms.
  • Familiarity with responsible AI practices, AI governance frameworks, and data privacy regulations.

SKILLS AND COMPETENCY REQUIREMENTS

  • Ownership: Treats the Constituent Data Layer as a product with constituents, not a project with tasks.
  • Safety mindset: Designs for what agents must never do before optimizing what they can do.
  • Pragmatic shipping: Delivers working, governed capability iteratively rather than perfect systems slowly.
  • Cross-functional translation: Moves fluently between constituent problem language and engineering design.
  • Documentation discipline: Leaves every schema, agent, and policy legible to the next engineer.
  • Curiosity: Tracks the rapidly evolving agentic AI landscape and applies it judiciously.

TECHNOLOGY SKILLS

The Senior Agentic AI Engineer is expected to bring deep hands-on proficiency across the following technology domains.

AI & MACHINE LEARNING PLATFORMS

  • Large language model (LLM) platforms: OpenAI / Azure OpenAI, Anthropic Claude, AWS Bedrock, or equivalent.
  • Agent orchestration frameworks, function/tool calling, and multi-agent patterns.
  • Retrieval-augmented generation (RAG) architecture and vector database platforms.
  • AI ops tooling for model monitoring, versioning, drift detection, and deployment pipelines.

CLOUD & INFRASTRUCTURE

  • Cloud platforms: proficiency in Microsoft Azure, with fluency across AWS and Google Cloud.
  • Containerization and orchestration: Docker and Kubernetes.
  • Serverless and event-driven architecture patterns.

DATA & ANALYTICS

  • SQL, relational, and vector databases.
  • Data pipeline and transformation tooling.
  • Schema design, data contracts, and data governance practices.

SOFTWARE ENGINEERING & DEVOPS

  • Source control and collaboration: Git, GitHub, or GitLab, including PR workflows and code review practices.
  • CI/CD pipeline platforms: GitHub, Azure DevOps.
  • API design and management; identity and access patterns (OAuth 2.0, SSO/SAML).
  • Agile and project management tooling: Jira, Confluence (Atlassian suite experience preferred).

PRODUCTIVITY & COLLABORATION

  • Microsoft 365 suite: Teams, SharePoint, Excel, and PowerPoint.
  • AI productivity tools: Microsoft Copilot, Anthropic Claude, or similar for personal and team enablement.

COMPENSATION

Salary range: $140,000 - $170,000, dependent upon experience

PHYSICAL DEMANDS

While performing the duties of this job, the employee is regularly required to sit; use hands to operate a computer, copier, fax or other office equipment. The employee must occasionally lift and/or move up to 15 pounds.

WORK ENVIRONMENT

Assumes a hybrid work environment located in Rochester, New York.

While performing the duties of this job, the noise level in the work environment is usually quiet to moderate.

TRAVEL

Up to 10%, Domestic (as required)

Worker Type:

Regular

Number of Openings Available:

1

Be part of something big. Apply to join our team today

Apply