Entry level AI/ML Engineer: SVL
Lensa
•
San Jose, CA
0
0
0
0
Not Applicable
Posted March 13, 2026
Job link
Thinking about this job
Responsibilities
Commitments
Responsibilities
- Data Collection and Management for LLM Evaluation and Training
- Design and implement robust data collection pipelines for diverse LLM training datasets leveraging the IBM AI Model & Data Catalog.
- Develop data quality assessment frameworks to ensure training data meets IBM's high standards
- Create annotation guidelines and workflows for specialized domain-specific datasets
- Implement data governance protocols that ensure compliance with privacy regulations and ethical AI principles following the IBM Data & Model Governance process and tooling.
- Establish evaluation datasets and benchmarks to measure LLM performance across various use cases leveraging [1] FM-Eval and Unitxt.
- LLM Integration and Implementation
- Architect solutions to integrate LLMs with IBM's existing and emerging products and ecosystem
- Develop APIs and interfaces that enable seamless interaction between LLMs and other software components
- Optimize LLM deployment for various computing environments (cloud, edge, on-premises)
- Implement techniques for model compression, quantization, and optimization to improve inference efficiency and minimize resource requirements
- Design and implement prompt engineering frameworks for consistent LLM behavior across products
- AI/ML Best Practices and Innovation
- Establish technical standards and best practices for AI/ML feature implementation
- Create reusable components and design patterns for common LLM use cases
- Develop monitoring systems to track model performance, drift, and potential biases
- Research and implement techniques for responsible AI, including explainability and fairness
- Collaborate with product teams to identify opportunities for AI-driven innovation
Commitments
Optimize LLM deployment for various computing environments (cloud, edge, on-premises)
IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.
Not Met Priorities
What still needs stronger evidence
Requirements
- Less than one year of experience in machine learning engineering or data science roles
- Demonstrated knowledge of NLP and large language models (e.g., transformer architectures) including model evaluation and algorithm design.
- Strong programming skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, or JAX)
- Experience with data processing pipelines and working with large datasets
- Knowledge of MLOps practices and tools for model deployment and monitoring
- Ability to work independently and collaborate effectively across diverse teams.
- Strong communication skills to explain complex AI concepts to diverse audiences
- Problem-solving mindset with ability to navigate technical and business constraints
- Proficiency in vector databases and embedding technologies
- Familiarity with cloud platforms (IBM Cloud, AWS, Azure, GCP)
Preferred Skills
- Experience with fine-tuning and prompt engineering for LLMs
- Deep understanding of transformer architectures and attention mechanisms
- Proficiency in vector databases and embedding technologies
- Knowledge of model serving frameworks (TensorRT, ONNX, TorchServe)
- Familiarity with cloud platforms (IBM Cloud, AWS, Azure, GCP)
- Commitment to continuous learning in the rapidly evolving field of AI
Education
- (Not required) – Bachelor's or master's degree or higher in Computer Science, Machine Learning, AI, or related technical field
Lensa is a career site that helps job seekers find great jobs in the US. We are not a staffing firm or agency. Lensa does not hire directly for these jobs, but promotes jobs on LinkedIn on behalf of its direct clients, recruitment ad agencies, and marketing partners. Lensa partners with DirectEmployers to promote this job for IBM. Clicking "Apply Now" or "Read more" on Lensa redirects you to the job board/employer site. Any information collected there is subject to their terms and privacy notice.
Introduction
IBM Automation and AI group is seeking an entry level AI/ML Engineer specializing in machine learning and Large Language Models (LLMs) to join our AI/ML Center of Excellence team at the IBM Silicon Valley Lab location. In this role, you will collaborate with cross-functional product teams to implement cutting-edge AI/ML capabilities across IBM's Automation product portfolio. You will lead efforts in data collection for LLM training and evaluation, integrate LLM technologies into existing and new products, and establish best practices for AI/ML feature implementation by product teams.
Your Role And Responsibilities
Data Collection and Management for LLM Evaluation and Training
Design and implement robust data collection pipelines for diverse LLM training datasets leveraging the IBM AI Model & Data Catalog.
Develop data quality assessment frameworks to ensure training data meets IBM's high standards
Create annotation guidelines and workflows for specialized domain-specific datasets
Implement data governance protocols that ensure compliance with privacy regulations and ethical AI principles following the IBM Data & Model Governance process and tooling.
Establish evaluation datasets and benchmarks to measure LLM performance across various use cases leveraging [1] FM-Eval and Unitxt.
LLM Integration and Implementation
Architect solutions to integrate LLMs with IBM's existing and emerging products and ecosystem
Develop APIs and interfaces that enable seamless interaction between LLMs and other software components
Optimize LLM deployment for various computing environments (cloud, edge, on-premises)
Implement techniques for model compression, quantization, and optimization to improve inference efficiency and minimize resource requirements
Design and implement prompt engineering frameworks for consistent LLM behavior across products
AI/ML Best Practices and Innovation
Establish technical standards and best practices for AI/ML feature implementation
Create reusable components and design patterns for common LLM use cases
Develop monitoring systems to track model performance, drift, and potential biases
Research and implement techniques for responsible AI, including explainability and fairness
Collaborate with product teams to identify opportunities for AI-driven innovation
References
Visible links
https://github.ibm.com/IBM-Research-AI/fm-eval
Required Technical And Professional Expertise
Bachelor's or master's degree or higher in Computer Science, Machine Learning, AI, or related technical field
Less than one year of experience in machine learning engineering or data science roles
Demonstrated knowledge of NLP and large language models (e.g., transformer architectures) including model evaluation and algorithm design.
Strong programming skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, or JAX)
Experience with data processing pipelines and working with large datasets
Knowledge of MLOps practices and tools for model deployment and monitoring
Ability to work independently and collaborate effectively across diverse teams.
Strong communication skills to explain complex AI concepts to diverse audiences
Problem-solving mindset with ability to navigate technical and business constraints
Preferred Technical And Professional Experience
Experience with fine-tuning and prompt engineering for LLMs
Deep understanding of transformer architectures and attention mechanisms
Proficiency in vector databases and embedding technologies
Knowledge of model serving frameworks (TensorRT, ONNX, TorchServe)
Familiarity with cloud platforms (IBM Cloud, AWS, Azure, GCP)
Commitment to continuous learning in the rapidly evolving field of AI
IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.
If you have questions about this posting, please contact support@lensa.com
Introduction
IBM Automation and AI group is seeking an entry level AI/ML Engineer specializing in machine learning and Large Language Models (LLMs) to join our AI/ML Center of Excellence team at the IBM Silicon Valley Lab location. In this role, you will collaborate with cross-functional product teams to implement cutting-edge AI/ML capabilities across IBM's Automation product portfolio. You will lead efforts in data collection for LLM training and evaluation, integrate LLM technologies into existing and new products, and establish best practices for AI/ML feature implementation by product teams.
Your Role And Responsibilities
Data Collection and Management for LLM Evaluation and Training
Design and implement robust data collection pipelines for diverse LLM training datasets leveraging the IBM AI Model & Data Catalog.
Develop data quality assessment frameworks to ensure training data meets IBM's high standards
Create annotation guidelines and workflows for specialized domain-specific datasets
Implement data governance protocols that ensure compliance with privacy regulations and ethical AI principles following the IBM Data & Model Governance process and tooling.
Establish evaluation datasets and benchmarks to measure LLM performance across various use cases leveraging [1] FM-Eval and Unitxt.
LLM Integration and Implementation
Architect solutions to integrate LLMs with IBM's existing and emerging products and ecosystem
Develop APIs and interfaces that enable seamless interaction between LLMs and other software components
Optimize LLM deployment for various computing environments (cloud, edge, on-premises)
Implement techniques for model compression, quantization, and optimization to improve inference efficiency and minimize resource requirements
Design and implement prompt engineering frameworks for consistent LLM behavior across products
AI/ML Best Practices and Innovation
Establish technical standards and best practices for AI/ML feature implementation
Create reusable components and design patterns for common LLM use cases
Develop monitoring systems to track model performance, drift, and potential biases
Research and implement techniques for responsible AI, including explainability and fairness
Collaborate with product teams to identify opportunities for AI-driven innovation
References
Visible links
https://github.ibm.com/IBM-Research-AI/fm-eval
Required Technical And Professional Expertise
Bachelor's or master's degree or higher in Computer Science, Machine Learning, AI, or related technical field
Less than one year of experience in machine learning engineering or data science roles
Demonstrated knowledge of NLP and large language models (e.g., transformer architectures) including model evaluation and algorithm design.
Strong programming skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, or JAX)
Experience with data processing pipelines and working with large datasets
Knowledge of MLOps practices and tools for model deployment and monitoring
Ability to work independently and collaborate effectively across diverse teams.
Strong communication skills to explain complex AI concepts to diverse audiences
Problem-solving mindset with ability to navigate technical and business constraints
Preferred Technical And Professional Experience
Experience with fine-tuning and prompt engineering for LLMs
Deep understanding of transformer architectures and attention mechanisms
Proficiency in vector databases and embedding technologies
Knowledge of model serving frameworks (TensorRT, ONNX, TorchServe)
Familiarity with cloud platforms (IBM Cloud, AWS, Azure, GCP)
Commitment to continuous learning in the rapidly evolving field of AI
IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.
If you have questions about this posting, please contact support@lensa.com