BI Engineer Internships
Bi engineer internships give university students, recent graduates, and early-career switchers hands-on project experience building dashboards, data pipelines, and reporting systems alongside working engineers, and, at many employers, a path toward a full-time offer. Openings are concentrated in Banking & Financial Services and Consulting & Professional Services, with Navy Federal Credit Union, Sandia National Laboratories, and Inter Miami CF among the employers posting roles now.
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[2027 Summer] AI Research and Engineering Intern for Business Intelligence (8023)
Overview of Role
Frontier models can now reason, write code, and use tools. The next step is getting agents to work reliably over long tasks with real data, and serving them efficiently in production. Enterprise business intelligence is one of the hardest places to test this. Agents must work through messy schemas and ambiguous questions. They have to keep business logic consistent across many steps, and they have to give answers someone can check. At a company operating at TSMC's scale, wrong numbers have real costs.
AI4BI Innovation center is looking for exceptional PhD and Master's students to work on this problem with us. You will own a research and engineering question end-to-end. That means designing the method, building the agent harness and environment, training or adapting models, deploying them, and measuring progress with evaluations that hold up. The goal is a first-author paper at a top venue (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP) or a prototype that ships into real decision-making workflows for business stakeholders. Many projects do both.
This is not a typical software engineering internship. You will have dedicated mentorship from AI Scientists and Data engineers, and access to realistic enterprise-scale data and business problems that are hard to get in academia.
What You'll Work On
Your project will fall within one or more of these directions. Each is grounded in real business problems.
- Agent harnesses and long-horizon agents. Design the harness around the model: the agent loop, tool and sandbox interfaces, context management and compaction, memory, sub-agent delegation, permissions, and error recovery. Build agents that can investigate a business question across hours of work. They explore data, form hypotheses, run analyses, and produce a defensible report.
- Tool use and multi-agent systems. Design agents that operate over warehouses, semantic layers, dashboards, and internal APIs through standard tool protocols such as the Model Context Protocol (MCP). Study when multi-agent orchestration helps a single strong agent, and when it gets in the way.
- Pretrained time-series foundation models. Evaluate, adapt, and fine-tune pretrained time-series foundation models (e.g., TimesFM, Chronos, Moirai) for forecasting demand, capacity, and supply-chain signals. Topics include zero-shot versus fine-tuned performance, covariates and multivariate forecasting, probabilistic forecasts and calibration, and domain-adaptive pretraining on enterprise time series. Study how LLM agents can call, combine, and explain these forecasting models, alongside optimization and network science methods.
- AI infrastructure and deployment. Build the systems that take models and agents from prototype to production. Topics include efficient LLM serving and inference optimization (batching, KV-cache management, quantization, speculative decoding), model routing and cost/latency trade-offs, on-premise and secure deployment, and observability and tracing for agent runs (including guardrails and monitoring).
- Agent environments and evaluation science. Build realistic, reproducible environments and benchmarks for data agents. Develop reliable LLM-as-judge and rubric-based evaluators. Measure what matters: correctness, consistency, calibration, latency, cost, and trustworthiness.
- Synthetic data and post-training. Generate verifiable training and evaluation tasks at scale. Adapt models to the enterprise domain through fine-tuning, distillation into small, cost-efficient models that can be deployed, and, where useful, reinforcement learning from verifiable signals such as SQL results and metric consistency checks.
Responsibilities
- Lead a research and engineering project. Frame an open problem, form hypotheses, and run careful experiments, from small-scale ablations up to training and serving on GPUs.
- Develop models and agents. Build and manage advanced machine learning, deep learning, LLM, foundation model, and time-series methods. Use them to develop data-driven solutions to complex business problems, and implement agent harnesses, tool interfaces, and environments grounded in realistic BI workloads.
- Deploy and operate. Package, serve, and monitor models and agents in production-like environments. Optimize them for reliability, latency, and cost.
- Turn data into decisions. Analyze and interpret complex data sets. Translate the results into actionable insights that improve decision-making for business stakeholders.
- Evaluate rigorously. Design benchmarks and evaluation protocols that measure reliability, not just demo quality. Report negative results honestly.
- Collaborate across teams and borders. Work with cross-functional and international teams in the US and Taiwan to find and prioritize opportunities to use data. Partner with research scientists and data engineers to develop and deploy predictive models and agentic systems. Help build and maintain efficient processes for collecting, storing, and analyzing data. Take part in weekly research syncs, paper reading groups, and brainstorming sessions.
- Communicate your work. Present insights and recommendations to both technical and non-technical stakeholders through internal reports and presentations. Write up findings for publication and/or internal deployment.
Minimum Qualifications
- Education: Currently pursuing a Ph.D. or M.S. in Computer Science, Electrical Engineering, Information Systems, Information Science, Statistics, or a related AI/ML field.
- AI-enabled analytics knowledge: Depth in one or more of machine learning, deep learning, large language models, AI agents, time-series modeling and forecasting, optimization, network science, or ML systems.
- Engineering: Strong Python skills and hands-on experience building and shipping ML or LLM systems.
- Research ability: Able to read recent papers critically, then implement and extend them quickly.
Preferred Qualifications
- Publications in leading AI conferences (e.g., NeurIPS, ICLR, ICML, ACL, KDD, MLSys) or strong pre-prints in LLMs, agents, time series, ML systems, or data management / ML for databases.
- Experience designing agent harnesses or building agents with modern agent SDKs and tool protocols (e.g., MCP), including context management, tool sandboxing, and agent tracing/observability.
- Experience with pretrained time-series foundation models (e.g., TimesFM, Chronos, Moirai) or deep forecasting methods, including fine-tuning and probabilistic evaluation.
- Experience with LLM serving and inference engines (e.g., vLLM, SGLang, TensorRT-LLM), model quantization, or inference optimization.
- Experience with MLOps and deployment infrastructure (e.g., Docker/Kubernetes, Ray, CI/CD for models, monitoring), including on-premise or GPU cluster environments.
- Hands-on LLM experience beyond prompting, such as fine-tuning, preference optimization, distillation, or RL post-training.
- Experience deploying models into user-facing tools, such as dashboarding platforms (e.g., Power BI), Python prototyping frameworks (e.g., Streamlit, Dash), and/or JavaScript frameworks (e.g., React).
- Experience with text-to-SQL, semantic parsing, tabular reasoning, forecasting, or analytics.
- Contributions to open-source AI/ML projects or public benchmarks.
Company Description
Join TSMC Technology, Inc. (TTI), an innovation hub of Taiwan Semiconductor Manufacturing Company Limited (TSMC). TSMC has been a pioneer in the semiconductor industry since 1987. With teams located in San Jose, CA, and Austin, TX, TTI is dedicated to advancing semiconductor technology through expertise in physical design, memory design, analog mixed signal, power management, and groundbreaking computational AI machine learning research. Our mission is to drive the future of semiconductor innovation through unparalleled R&D and customer-focused IP. Be part of a team that shapes the next generation of technological advancements.
As a trusted technology and capacity provider, TSMC is driven by the desire to be:
- The world’s leading dedicated semiconductor foundry
- The technology leader with a strong reputation for manufacturing excellence
- Advancing semiconductor manufacturing innovations to enable the future of technology
TSMC pioneered the pure-play foundry business model when it was founded in 1987 and has been the world’s leading dedicated semiconductor foundry ever since. The Company supports a thriving ecosystem of global customers and partners with the industry’s leading process technologies and a portfolio of design enablement solutions to unleash innovation for the global semiconductor industry. With global operations spanning Asia, Europe, and North America, TSMC serves as a committed corporate citizen around the world.
In North America, TSMC has a strong sales and service organization that works with customers by helping them achieve silicon success with cutting-edge technologies and manufacturing excellence. The Company has continued to accelerate its R&D investment and staffing in recent years and is expanding its manufacturing footprint to support customer innovation with 3D IC technologies and optimal manufacturing capacity.
For positions requiring access to technical data subject to export control regulations, including Export Administration Regulations, TSMC Technology Inc. may have to obtain export licensing approval from the U.S. Government for certain individuals. All employment is contingent upon TSMC Technology Inc. obtaining any export license or other approval that may be required by the U.S. Government.
TSMC Core Values
At TSMC, we invite talented individuals who share our vision and values to contribute to our continued success. Our core values are the foundation of our culture, guiding every decision and action. We seek applicants who deeply resonate with these principles and embody them in their work:
- Integrity: Tell the truth. We do not brag. We do not make commitments lightly. Once we make a commitment, we devote ourselves completely to meeting that commitment.
- Commitment: Employees are dedicated to the company, view the company’s success as their own and work diligently to make their best contributions. As commitment is mutual, the Company strives to serve the best interests of its employees.
- Innovation: Innovation is the wellspring of the Company's growth. It means more than new ideas; it means putting ideas into practice.
- Customer Trust: We strive to build deep and enduring relationships with our customers, who trust and rely on us to be part of their success over the long term.
(For more details on TSMC’s Core Values, please visit: https://www.tsmc.com/english/aboutTSMC/values)
Diversity statement
TSMC Technology, Inc. is committed to employing a diverse workforce and provides Equal Employment Opportunity for all individuals regardless of race, color, religion, gender, age, national origin, marital status, sexual orientation, gender identity, status as a protected veteran, genetic information, or any other characteristic protected by applicable law.
TSMC is an equal opportunity employer prizing diversity and inclusion. We are committed to treating all employees and applicants for employment with respect and dignity. If you require reasonable accommodation due to a disability during the application or the recruiting process, please feel free to notify us at g_accommodations@tsmc.com. TSMC confirms to all applicants its commitment to meet TSMC’s obligations under applicable employment law. Reasonable accommodations will be determined on a case-by-case basis.
Pay Transparency Statement
At the time of this posting, this role typically pays an hourly rate between $41 and $52 per hour. The range displayed reflects the minimum and maximum target for new hires. Actual pay may be more or less than the posted range. Factors that influence pay include the individual's skills, qualifications, education, experience and the position level and location.
Nearest Major Market: San Jose
Nearest Secondary Market: Palo Alto
[2027 Summer] AI Research and Engineering Intern for Business Intelligence (8023)
Overview of Role
Frontier models can now reason, write code, and use tools. The next step is getting agents to work reliably over long tasks with real data, and serving them efficiently in production. Enterprise business intelligence is one of the hardest places to test this. Agents must work through messy schemas and ambiguous questions. They have to keep business logic consistent across many steps, and they have to give answers someone can check. At a company operating at TSMC's scale, wrong numbers have real costs.
AI4BI Innovation center is looking for exceptional PhD and Master's students to work on this problem with us. You will own a research and engineering question end-to-end. That means designing the method, building the agent harness and environment, training or adapting models, deploying them, and measuring progress with evaluations that hold up. The goal is a first-author paper at a top venue (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP) or a prototype that ships into real decision-making workflows for business stakeholders. Many projects do both.
This is not a typical software engineering internship. You will have dedicated mentorship from AI Scientists and Data engineers, and access to realistic enterprise-scale data and business problems that are hard to get in academia.
What You'll Work On
Your project will fall within one or more of these directions. Each is grounded in real business problems.
- Agent harnesses and long-horizon agents. Design the harness around the model: the agent loop, tool and sandbox interfaces, context management and compaction, memory, sub-agent delegation, permissions, and error recovery. Build agents that can investigate a business question across hours of work. They explore data, form hypotheses, run analyses, and produce a defensible report.
- Tool use and multi-agent systems. Design agents that operate over warehouses, semantic layers, dashboards, and internal APIs through standard tool protocols such as the Model Context Protocol (MCP). Study when multi-agent orchestration helps a single strong agent, and when it gets in the way.
- Pretrained time-series foundation models. Evaluate, adapt, and fine-tune pretrained time-series foundation models (e.g., TimesFM, Chronos, Moirai) for forecasting demand, capacity, and supply-chain signals. Topics include zero-shot versus fine-tuned performance, covariates and multivariate forecasting, probabilistic forecasts and calibration, and domain-adaptive pretraining on enterprise time series. Study how LLM agents can call, combine, and explain these forecasting models, alongside optimization and network science methods.
- AI infrastructure and deployment. Build the systems that take models and agents from prototype to production. Topics include efficient LLM serving and inference optimization (batching, KV-cache management, quantization, speculative decoding), model routing and cost/latency trade-offs, on-premise and secure deployment, and observability and tracing for agent runs (including guardrails and monitoring).
- Agent environments and evaluation science. Build realistic, reproducible environments and benchmarks for data agents. Develop reliable LLM-as-judge and rubric-based evaluators. Measure what matters: correctness, consistency, calibration, latency, cost, and trustworthiness.
- Synthetic data and post-training. Generate verifiable training and evaluation tasks at scale. Adapt models to the enterprise domain through fine-tuning, distillation into small, cost-efficient models that can be deployed, and, where useful, reinforcement learning from verifiable signals such as SQL results and metric consistency checks.
Responsibilities
- Lead a research and engineering project. Frame an open problem, form hypotheses, and run careful experiments, from small-scale ablations up to training and serving on GPUs.
- Develop models and agents. Build and manage advanced machine learning, deep learning, LLM, foundation model, and time-series methods. Use them to develop data-driven solutions to complex business problems, and implement agent harnesses, tool interfaces, and environments grounded in realistic BI workloads.
- Deploy and operate. Package, serve, and monitor models and agents in production-like environments. Optimize them for reliability, latency, and cost.
- Turn data into decisions. Analyze and interpret complex data sets. Translate the results into actionable insights that improve decision-making for business stakeholders.
- Evaluate rigorously. Design benchmarks and evaluation protocols that measure reliability, not just demo quality. Report negative results honestly.
- Collaborate across teams and borders. Work with cross-functional and international teams in the US and Taiwan to find and prioritize opportunities to use data. Partner with research scientists and data engineers to develop and deploy predictive models and agentic systems. Help build and maintain efficient processes for collecting, storing, and analyzing data. Take part in weekly research syncs, paper reading groups, and brainstorming sessions.
- Communicate your work. Present insights and recommendations to both technical and non-technical stakeholders through internal reports and presentations. Write up findings for publication and/or internal deployment.
Minimum Qualifications
- Education: Currently pursuing a Ph.D. or M.S. in Computer Science, Electrical Engineering, Information Systems, Information Science, Statistics, or a related AI/ML field.
- AI-enabled analytics knowledge: Depth in one or more of machine learning, deep learning, large language models, AI agents, time-series modeling and forecasting, optimization, network science, or ML systems.
- Engineering: Strong Python skills and hands-on experience building and shipping ML or LLM systems.
- Research ability: Able to read recent papers critically, then implement and extend them quickly.
Preferred Qualifications
- Publications in leading AI conferences (e.g., NeurIPS, ICLR, ICML, ACL, KDD, MLSys) or strong pre-prints in LLMs, agents, time series, ML systems, or data management / ML for databases.
- Experience designing agent harnesses or building agents with modern agent SDKs and tool protocols (e.g., MCP), including context management, tool sandboxing, and agent tracing/observability.
- Experience with pretrained time-series foundation models (e.g., TimesFM, Chronos, Moirai) or deep forecasting methods, including fine-tuning and probabilistic evaluation.
- Experience with LLM serving and inference engines (e.g., vLLM, SGLang, TensorRT-LLM), model quantization, or inference optimization.
- Experience with MLOps and deployment infrastructure (e.g., Docker/Kubernetes, Ray, CI/CD for models, monitoring), including on-premise or GPU cluster environments.
- Hands-on LLM experience beyond prompting, such as fine-tuning, preference optimization, distillation, or RL post-training.
- Experience deploying models into user-facing tools, such as dashboarding platforms (e.g., Power BI), Python prototyping frameworks (e.g., Streamlit, Dash), and/or JavaScript frameworks (e.g., React).
- Experience with text-to-SQL, semantic parsing, tabular reasoning, forecasting, or analytics.
- Contributions to open-source AI/ML projects or public benchmarks.
Company Description
Join TSMC Technology, Inc. (TTI), an innovation hub of Taiwan Semiconductor Manufacturing Company Limited (TSMC). TSMC has been a pioneer in the semiconductor industry since 1987. With teams located in San Jose, CA, and Austin, TX, TTI is dedicated to advancing semiconductor technology through expertise in physical design, memory design, analog mixed signal, power management, and groundbreaking computational AI machine learning research. Our mission is to drive the future of semiconductor innovation through unparalleled R&D and customer-focused IP. Be part of a team that shapes the next generation of technological advancements.
As a trusted technology and capacity provider, TSMC is driven by the desire to be:
- The world’s leading dedicated semiconductor foundry
- The technology leader with a strong reputation for manufacturing excellence
- Advancing semiconductor manufacturing innovations to enable the future of technology
TSMC pioneered the pure-play foundry business model when it was founded in 1987 and has been the world’s leading dedicated semiconductor foundry ever since. The Company supports a thriving ecosystem of global customers and partners with the industry’s leading process technologies and a portfolio of design enablement solutions to unleash innovation for the global semiconductor industry. With global operations spanning Asia, Europe, and North America, TSMC serves as a committed corporate citizen around the world.
In North America, TSMC has a strong sales and service organization that works with customers by helping them achieve silicon success with cutting-edge technologies and manufacturing excellence. The Company has continued to accelerate its R&D investment and staffing in recent years and is expanding its manufacturing footprint to support customer innovation with 3D IC technologies and optimal manufacturing capacity.
For positions requiring access to technical data subject to export control regulations, including Export Administration Regulations, TSMC Technology Inc. may have to obtain export licensing approval from the U.S. Government for certain individuals. All employment is contingent upon TSMC Technology Inc. obtaining any export license or other approval that may be required by the U.S. Government.
TSMC Core Values
At TSMC, we invite talented individuals who share our vision and values to contribute to our continued success. Our core values are the foundation of our culture, guiding every decision and action. We seek applicants who deeply resonate with these principles and embody them in their work:
- Integrity: Tell the truth. We do not brag. We do not make commitments lightly. Once we make a commitment, we devote ourselves completely to meeting that commitment.
- Commitment: Employees are dedicated to the company, view the company’s success as their own and work diligently to make their best contributions. As commitment is mutual, the Company strives to serve the best interests of its employees.
- Innovation: Innovation is the wellspring of the Company's growth. It means more than new ideas; it means putting ideas into practice.
- Customer Trust: We strive to build deep and enduring relationships with our customers, who trust and rely on us to be part of their success over the long term.
(For more details on TSMC’s Core Values, please visit: https://www.tsmc.com/english/aboutTSMC/values)
Diversity statement
TSMC Technology, Inc. is committed to employing a diverse workforce and provides Equal Employment Opportunity for all individuals regardless of race, color, religion, gender, age, national origin, marital status, sexual orientation, gender identity, status as a protected veteran, genetic information, or any other characteristic protected by applicable law.
TSMC is an equal opportunity employer prizing diversity and inclusion. We are committed to treating all employees and applicants for employment with respect and dignity. If you require reasonable accommodation due to a disability during the application or the recruiting process, please feel free to notify us at g_accommodations@tsmc.com. TSMC confirms to all applicants its commitment to meet TSMC’s obligations under applicable employment law. Reasonable accommodations will be determined on a case-by-case basis.
Pay Transparency Statement
At the time of this posting, this role typically pays an hourly rate between $41 and $52 per hour. The range displayed reflects the minimum and maximum target for new hires. Actual pay may be more or less than the posted range. Factors that influence pay include the individual's skills, qualifications, education, experience and the position level and location.
Nearest Major Market: San Jose
Nearest Secondary Market: Palo Alto
BI Engineer Internship Market
Who's Hiring
Top Industries Hiring
- Banking & Financial Services
- Consulting & Professional Services
Tips for Your BI Engineer Internship Search
Apply earlier than the posting suggests
Large employers fill summer bi engineer intern cohorts the fall before the start date, often closing applications months before the role goes quiet online. Smaller companies and co-ops post much closer to the start date, so check listings regularly throughout the year rather than waiting for a single peak season.
Build a portfolio before you send a single application
Bi engineer intern hiring teams want to open something, a dashboard in Tableau or Power BI, a SQL project on a public dataset, a data pipeline documented in a GitHub repository. Two or three complete, linked projects give recruiters something concrete to assess when your work history is limited.
Work your campus network and apply directly at the same time
Campus career fairs surface structured bi engineer programs tied to your university, and professors or career center staff often know which employers recruit from your school before roles post publicly. Applying directly to companies running smaller cohorts alongside campus activity widens the pool you reach and reduces your dependence on any single channel.
Practice the technical screen out loud before applying
Bi engineer intern interviews typically include a technical screen covering SQL query writing, data modeling concepts, and sometimes a take-home analysis or dashboard exercise. Practice solving these problems while explaining your reasoning aloud, interviewers weigh how you think through the data as much as whether the final output is correct.
Target structured rotational and university cohort programs early
Larger companies with business intelligence functions often run formal cohort programs designed to train people new to the field, rotating interns across analytics, reporting, and data infrastructure teams. These programs recruit in the first wave of intern hiring and fill fast, so identify the ones that fit your background and apply before the general posting goes live.
Set your work-type filter before you start
On-site roles are 38% of the bi engineer internships listed here. Decide what you can commit to, on-site, hybrid, or remote, before you start searching, then filter by location and work type so you are not spending time on roles you cannot take.
BI Engineer Internships: Frequently Asked Questions
How do I get a bi engineer internship?
Lead with coursework and personal projects rather than work history, hiring teams expect limited experience at the intern level. For bi engineer candidates, the concrete artifact recruiters want to assess is a portfolio of dashboards, SQL queries, or data models they can actually open and review. Pair direct applications with campus career fairs, where recruiters often move faster for students they meet in person.
Can a bi engineer internship turn into a full-time job?
Many employers extend return offers to strong interns, but conversion is never guaranteed. For bi engineer interns, what actually drives it is the quality of your work on real projects, available headcount on the team at the time of your internship, and return-offer timing communicated near the end of your term. Position for one by delivering consistently, without counting on it as a certainty.
When should I apply for bi engineer internships?
Earlier than most expect. Large employers recruit summer bi engineer interns the preceding fall, sometimes as early as August or September. Smaller companies and co-op programs post much closer to the start date, so openings appear year-round rather than in a single window. Checking listings regularly means you won't miss roles that open outside the main recruiting season.
Are bi engineer internships paid?
Most professional bi engineer internships in the U.S. are paid. Compensation varies by company size, industry, and location, and listings show it where the employer chooses to disclose it. Unpaid arrangements are uncommon at established companies and are more frequently associated with small startups or nonprofit organizations.
What should a bi engineer internship resume include?
Lead with projects, not work history. Include two or three complete, documented projects that name the tools you used, SQL, Power BI, Tableau, Python, dbt, or whatever you actually worked with, and link to the work where possible, such as a GitHub repository or a published dashboard. Add relevant coursework, keep the whole document to one page, and put your strongest project first.
Are there remote bi engineer internships?
Yes. Remote and hybrid roles make up 62% of the bi engineer internship listings here, with the rest on-site. Remote cohorts fill fast because they draw applicants from across the country, so apply early once you have decided you are open to that format and filter by work type to see those listings first.
Can international students get bi engineer internships?
Yes. F-1 students can intern through CPT while enrolled or through OPT work authorization after finishing a degree, and the employer does not have to file anything for either, so many companies are open to international interns. Confirm your eligibility and timing with your university's international student office before accepting an offer.
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