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Scientific Machine Learning Jobs in Indiana (NOW HIRING)

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What cities in Indiana are hiring for Scientific Machine Learning jobs?

Cities in Indiana with the most Scientific Machine Learning job openings:

Infographic showing various Scientific Machine Learning job openings in Indiana as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 69% Full Time, 28% Part Time, and 1% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution.

AI Machine Learning Engineer Intern (BS)

Indianapolis, IN • On-site

$60 - $80/hr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 2 days ago

New


Job description

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Organization Overview

At Lilly, we serve an extraordinary purpose. We make a difference for people around the globe by discovering, developing and delivering medicines that help them live longer, healthier, more active lives. Not only do we deliver breakthrough medications, but you also can count on us to develop creative solutions to support communities through philanthropy and volunteerism.

Responsibilities

You will join the Clinical and Development area within Lilly’s Advanced Intelligence & Research organization, where we build and deliver advanced AI and data science solutions that accelerate clinical development and improve decision‑making across the drug development lifecycle. Machine learning is central to that work. We build, deploy, and scale models and AI systems that support decisions across clinical development, ranging from predictive models trained on clinical and real‑world data to generative AI applications and agentic systems that put those capabilities directly in the hands of scientists and clinicians. Our engineers work across the full lifecycle: framing the problem, prototyping, evaluating rigorously, and building the pipelines and infrastructure that take a promising model from a notebook to something people rely on. As an intern, you will be assigned a scoped project with real business impact and will work alongside experienced machine learning engineers and research scientists. The work will draw on a common set of capabilities: training, tuning, and evaluating machine learning models; building and scaling the pipelines that support them; writing production‑quality Python; and translating methods and results for scientific and business partners. You might build generative AI applications, AI agents, or Model Context Protocol (MCP) integrations; develop predictive models on clinical or real‑world data; help scale and productionize model training and deployment pipelines; or prototype and evaluate newer methods against real clinical development problems. Lilly internships run for 12 continuous weeks over the summer. Each intern actively contributes to the organization, builds a comprehensive understanding of the pharmaceutical industry, and takes part in professional development and social events throughout the summer. At the conclusion of the internship, each intern presents their project highlights, findings, recommendations, and accomplishments to senior leaders and stakeholders. As part of Lilly's commitment to innovation, interns will have the opportunity to build fluency with AI tools used across the business. We expect interns to approach these tools with curiosity, apply critical thinking to AI‑assisted work, and always prioritize accuracy, confidentiality, and ethical standards in how they use them.

Basic Qualifications

Currently enrolled in and pursuing a Bachelor’s degree in Computer Science, Computer Engineering, Data Science, Statistics, Mathematics, Applied Mathematics, Electrical Engineering, or a closely related technical field, and will have completed your Junior year by June 2027. Qualified applicants must be authorized to work in the United States on a full‑time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1CPT, F-1OPT, F-1STEMOPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.

Additional Skills / Preferences

Experience with at least one machine learning or deep learning framework, such as PyTorch, scikit-learn, or TensorFlow. Fundamental knowledge of machine learning principles and hands‑on experience programming in Python. Experience building applications with large language models, including prompt engineering, retrieval‑augmented generation, agent frameworks, Model Context Protocol (MCP), or systematic model evaluation. Experience training and evaluating predictive models on real datasets, including feature engineering, validation strategy, and interpreting results. Working knowledge of SQL and comfort handling large datasets; experience with Spark or PySpark, or a cloud data platform such as Databricks is a plus. Exposure to a major cloud provider, preferably AWS (for example SageMaker, Bedrock, S3, or Lambda). Familiarity with MLOps practices such as experiment tracking, model packaging and serving, monitoring, or workflow orchestration, using tools such as MLflow or Weights & Biases. Experience with Git‑based version control, code review, and automated testing or CI/CD; familiarity with Docker is a plus. Interest in healthcare, clinical, or life sciences applications of machine learning, or prior experience working with sensitive or regulated data. Ability to communicate methods and results clearly to both technical and non‑technical audiences. A demonstrated drive to learn, innovate, and challenge yourself for the benefit of patients. Prior experience using AI tools (e.g., generative AI platforms, automation tools, or AI‑assisted research/analytics tools) in an academic, project, or work setting.

Additional Information

This is a hands‑on applied machine learning engineering internship. Interns are expected to take ownership of a scoped project, write production‑quality code, collaborate closely with data engineers, research scientists, and clinical partners, and deliver a final presentation of their results to senior leaders and stakeholders. All interns will be considered for full‑time positions based on their internship performance. Lilly arranges various intern activities including sporting events, dinners, lunch and learns, volunteer activities etc. to provide opportunities for socializing, professional development, and learning more about Lilly. Interns will receive 1 week of paid time off during the Lilly summer shut‑down (July5th–July9th), 1:1 mentoring from an experienced professional in the function. Interns will receive a competitive salary and free parking at their work site, as well as access to Lilly’s LIFE fitness center, bike garage, and many other discounts. If the intern’s job position requires a move from another location, Lilly will provide subsidized housing. Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form https://careers.lilly.com/us/en/workplace-accommodation for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Employee Resource Groups (ERGs)

Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include:

  • Africa
  • Middle East
  • Central Asia (AMECA)
  • Black Employees at Lilly (BE@Lilly)
  • Chinese Culture Network (CCN)
  • EnAble
  • Evolve
  • Lilly Indian Network (LIN)
  • Organization of Latinx at Lilly (OLA)
  • Pride (LGBTQ+ Allies)
  • Veterans Leadership Network (VLN)
  • Women’s Initiative for Leading at Lilly (WILL)
EEO and Diversity

Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status. Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees.

Compensation and Benefits

Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is $65,600 (Bachelors) annually. Full‑time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company‑sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day‑care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well‑being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities). Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.

About Lilly

How do we do this? We continue to look for ways to include, innovate, accelerate and deliver while maintaining integrity, excellence and respect for people. We hope that you seek to join us on our journey as we create medicine and deliver improved outcomes for patients across the globe! #WeAreLilly

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