Senior Level AI ML Intern Jobs
Senior level ai ml intern jobs place experienced practitioners at the center of model architecture decisions, research roadmaps, and the cross-functional teams that bring ML systems to production. Openings concentrate across Technology & Software, Banking & Financial Services, and Retail, with 38% remote or hybrid availability, and employers like Oracle, JPMorganChase, and General Motors hiring at this level now.
Find JobsOverview
Showing 5 of 23+ Senior Level AI ML Intern jobs
INTRODUCTION
Provide hands-on technical leadership by designing, developing, and deploying ML/LLM/GenAI solutions from concept through production, maintaining ownership for reliability and operability once deployed. Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases. Mentor and uplift junior engineers through design reviews, code reviews, pairing, and coaching, raising engineering quality and delivery discipline across the team. You will build and institutionalize MLOps capabilities, including automated pipelines for deployment, monitoring, and model lifecycle management, with emphasis on scalability and reliability. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. Conduct thorough evaluations of generative models (e.g., GPT-4.1), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
BASIC QUALIFICATIONS
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 10+ years of engineering experience, including 3-5+ years building, deploying, and operating applied AI/ML systems in production (model lifecycle, MLOps, monitoring, and governance)
- Demonstrate hands-on engineering leadership: setting technical direction, making architecture decisions, conducting design and code reviews, mentoring junior engineers, and guiding implementation quality across multiple workstreams
- Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API
- Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering
PREFERRED QUALIFICATIONS
- Familiarity with the financial services industries
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG)
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies
INTRODUCTION
Provide hands-on technical leadership by designing, developing, and deploying ML/LLM/GenAI solutions from concept through production, maintaining ownership for reliability and operability once deployed. Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases. Mentor and uplift junior engineers through design reviews, code reviews, pairing, and coaching, raising engineering quality and delivery discipline across the team. You will build and institutionalize MLOps capabilities, including automated pipelines for deployment, monitoring, and model lifecycle management, with emphasis on scalability and reliability. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. Conduct thorough evaluations of generative models (e.g., GPT-4.1), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
BASIC QUALIFICATIONS
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 10+ years of engineering experience, including 3-5+ years building, deploying, and operating applied AI/ML systems in production (model lifecycle, MLOps, monitoring, and governance)
- Demonstrate hands-on engineering leadership: setting technical direction, making architecture decisions, conducting design and code reviews, mentoring junior engineers, and guiding implementation quality across multiple workstreams
- Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API
- Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering
PREFERRED QUALIFICATIONS
- Familiarity with the financial services industries
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG)
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies
See All 23 Senior Level AI ML Intern Jobs
Find roles that match your experience and apply in just a few clicks.
Find JobsSenior Level AI ML Intern Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software10
- Banking & Financial Services4
- Retail2
- Automotive2
- Electronics & Hardware2
Senior Level AI ML Intern Jobs: Frequently Asked Questions
How do I get a senior level ai ml intern job?
Employers hiring at this level look for candidates who have led end-to-end ML projects, not just contributed to them. A strong portfolio of deployed models, evidence of mentoring junior researchers, and the ability to translate ambiguous business problems into concrete ML solutions all give candidates a clear edge. Publications, open-source contributions, or demonstrated cross-team technical leadership strengthen an application considerably.
Which companies hire senior level ai ml interns?
Companies hiring senior level ai ml interns right now include Oracle, JPMorganChase, and General Motors, based on current listings on Migrate Mate as of August 2026. Hiring at this level tends to come from organizations running active research programs, scaling production ML infrastructure, or building out specialized AI teams that need experienced practitioners who can operate with significant autonomy.
Are there remote senior level ai ml intern jobs?
Yes, remote availability is meaningful at this level given the seniority and autonomy these roles carry. About 38% of senior level ai ml intern openings are remote or hybrid as of August 2026, reflecting how many organizations have structured senior research and engineering work around distributed teams. Filtering by work setting on Migrate Mate helps narrow openings to your preferred arrangement quickly.
What makes a ai ml intern role senior level?
Senior level ai ml intern roles are defined by scope and ownership rather than task execution. Candidates are expected to set technical direction, own the full lifecycle of models from research through deployment, and mentor mid-level and junior colleagues. The work typically involves ambiguous problem framing, cross-functional stakeholder alignment, and decisions that carry organizational weight rather than individual contribution.
Which industries hire the most senior level ai ml interns?
Senior Level ai ml intern roles concentrate in Technology & Software, Banking & Financial Services, and Retail, based on current listings on Migrate Mate as of August 2026. These sectors tend to drive hiring at this level because they operate at the frontier of applied ML, whether that means large-scale recommendation systems, scientific research, autonomous systems, or enterprise AI products requiring deep technical expertise to build and maintain.