LLM Research Scientist

DESCRIPTION

GPP Database Link (https://cummins365.sharepoint.com/sites/CS38534/)

Job Summary:

Solves complex analytical problems using quantitative approaches through a combination of analytical, mathematical and technical skills. Researches, designs, implements and validates complex algorithms to analyze diverse sources of data to achieve targeted outcomes by leveraging complex statistical and predictive modeling concepts.

Key Responsibilities:

Participates in projects to support key objectives and business goals through the use of data science methodology. Leverages data science methodology to solve complex business problems. Creates multiple algorithms using complex statistical methodologies through the use of statistical programming languages and tools. Partners with domain experts to verify model capabilities. Partners with Solution Architect to enable appropriate data flow/data model, development using appropriate tools/technology, rapid prototyping and informs the design of analytical products. Partners with less experienced employees on data science tools and methodologies. Clearly articulates results, methodologies and learnings to stakeholder and peer group. Continuous development and advancement of the team through knowledge sharing and collaboration.

RESPONSIBILITIES

Competencies:

Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.

Customer focus - Building strong customer relationships and delivering customer-centric solutions.

Decision quality - Making good and timely decisions that keep the organization moving forward.

Manages complexity - Making sense of complex, high quantity, and sometimes contradictory information to effectively solve problems.

Tech savvy - Anticipating and adopting innovations in business-building digital and technology applications.

Data Mining - Extracts insights from data by identifying relationships and patterns through use of a suite of data exploration and data visualization techniques to understand the underlying structure of the data and enable sound conclusions upon model building.

Predictive Modeling - Develops analytical or machine learning models by using appropriate variable transformations, feature selection strategies, imputation strategies, class rebalancing, resampling strategies and quality control measures to generate predictive insights used in solving business questions.

Programming - Creates, writes and tests computer code, test scripts, and build scripts using algorithmic analysis and design, industry standards and tools, version control, and build and test automation to meet business, technical, security, governance and compliance requirements.

Requirements Analysis - Evaluates relationships and interdependencies between requirements based upon their complexity and value to the business in order to determine feasibility and prioritization.

Statistical Modeling - Develops descriptive and explanatory statistical models, and simulations for regression, classification, outlier detection, anomaly detection, time series forecasting using knowledge of foundational statistics such as null hypotheses significance tests, regression models, generalized linear modeling, time series analysis, rank statistics, probability distribution fitting survival analysis, etc. to validate hypotheses for any given statistical or business question.

Problem Solving - Solves problems and may mentor others on effective problem solving by using a systematic analysis process by leveraging industry standard methodologies to create problem traceability and protect the customer; determines the assignable cause; implements robust, data-based solutions; identifies the systemic root causes and ensures actions to prevent problem reoccurrence are implemented.

Values differences - Recognizing the value that different perspectives and cultures bring to an organization.

Education, Licenses, Certifications:

College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required. This position may require licensing for compliance with export controls or sanctions regulations.

Experience:

Intermediate experience in a relevant discipline area is required with a demonstrated track record of analyzing complex business systems and large data sets. Knowledge of the latest technologies and trends in data science is highly preferred and includes:

• Familiarity analyzing complex business systems, industry requirements, and/or data regulations
• Background in processing and managing large data sets
• Applied knowledge of big data, open source and third party toolsets
• SQL query language
• Clustered compute cloud-based implementation experience
• Experience in building analytical solutions

Intermediate experiences in the following are preferred:

• Implementing Big Data platform solutions using open source and third-party tools
• Microsoft Azure and/or Amazon Web services environment
• Experience in Agile software development
• Familiarity with validation and testing of machine learning systems
• Familiarity with Continuous Integration and Continuous Delivery (CI/CD)

QUALIFICATIONS

🌐 JD — LLM Research Scientist (Senior Level)

Cummins AI Lab – Artificial Intelligence Laboratory

About Cummins AI Lab

The Cummins AI Lab is a global innovation hub dedicated to advancing frontier AI capabilities and deploying AI solutions across Cummins’ engineering, manufacturing, service, and industrial operations worldwide.

We focus on applied LLM research , domain-specific model development, and end-to-end enterprise AI transformation.

We are seeking a Senior LLM Research Scientist to drive advanced research, design next-generation domain LLMs, and influence the AI Lab’s future technical direction.

Role Overview

This is a senior-level, high-autonomy role requiring deep expertise in large language models, applied AI research, and complex problem-solving. You will explore the latest LLM advancements, design LLM-driven solutions tailored to Cummins’ engineering and industrial scenarios, and play a central role in building our domain-specific enterprise LLM.

Candidates must demonstrate strong applied research capabilities, cross-disciplinary thinking, and a proven ability to bring research into real-world production.

Key Responsibilities

1. Frontier LLM Research & Technology Exploration

• Investigate state-of-the-art LLM and multimodal technologies: RAG, Agents, tool use, reasoning, alignment, knowledge injection, and domain adaptation.
• Analyze global research trends and define how emerging techniques can be applied to Cummins’ engineering and industrial workflows.
• Lead deep technical explorations and novel architecture experimentation.

2. Domain-Specific LLM Development

• Architect domain-grounded LLM solutions for engineering design, simulation workflows, diagnostics, service operations, manufacturing, and technical documentation.
• Build pipelines for SFT/IFT, retrieval-augmented reasoning, prompt engineering, and knowledge-grounded inference.
• Integrate structured and unstructured engineering knowledge (CAD/CAE outputs, diagnostic logs, manuals, sensor data).

3. Cross-Disciplinary Innovation

• Collaborate with engineering, simulation, materials, control systems, quality, and service teams to design innovative AI workflows.
• Apply multi-domain expertise to create LLM-powered tools for engineering productivity, automated reasoning, and smart decision-making.

4. End-to-End Research Delivery

• Own the full cycle: exploration → prototyping → evaluation → documentation.
• Produce technical reports, architecture designs, and executive-level presentations.
• Provide technical leadership in transitioning prototypes to enterprise systems.

Must-Have Qualifications

• Master’s or PhD in Computer Science, AI, Machine Learning, Engineering, or related fields; OR equivalent senior-level research experience.
• Deep expertise in LLM technologies: Transformer, SFT/IFT, RAG, agents, tool-use, reasoning, alignment, evaluation.
• Proven experience shipping applied AI/LLM systems beyond PoC into real-world environments.
• Strong programming and experimentation skills (Python, PyTorch, HuggingFace, LangChain, LlamaIndex).
• Experience integrating LLMs with engineering, scientific, or industrial domains.
• Demonstrated ability to independently lead complex technical explorations.
• Strong communication skills for working with global research and engineering teams.

Preferred Qualifications

• Experience in AI Labs, cloud providers, or major technology companies.
• Experience building domain-specific or enterprise LLMs.
• Familiarity with CAD/CAE/PLM systems (Creo, SolidWorks, CATIA, Ansys).
• Publications, patents, or open-source contributions.

What You Will Gain

• Opportunity to shape Cummins’ domain LLM strategy and long-term AI roadmap.
• Work on high-impact problems across engineering, manufacturing, and industrial operations.
• Collaboration with global experts across AI, product, engineering, and platform teams.

Job Systems/Information Technology

Organization Cummins Inc.

Role Category On-site with Flexibility

Job Type Exempt - Experienced

ReqID 2426827

Relocation Package No

100% On-Site No