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Multimodal Learning Jobs in Indianapolis, IN (NOW HIRING)

Cisco

Applied AI Scientist

Indianapolis, IN · On-site

... multimodal machinegenerated data - including logs, time series, traces, and events! We combine deep ... Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE ...

Elanco

Advisor R&D

Indianapolis, IN

As the Senior Machine Learning & Computer Vision Scientist, you will accelerate Elanco's R&D ... multimodal data (molecular, assay, imaging, behavioral) to deliver reliable predictions and ...

All JobsMultimodal Learning JobsMultimodal Learning Jobs in Indianapolis, IN

Multimodal Learning information

See Indianapolis, IN salary details

$20.1K

$59K

$109.4K

How much do multimodal learning jobs pay per year?

As of Aug 31, 2026, the average yearly pay for multimodal learning in Indianapolis, IN is $58,969.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,200.00 and $68,800.00 per year, depending on experience, location, and employer.

What is multimodal learning?

Multimodal learning is an area of machine learning that involves integrating and processing information from multiple types of data, such as text, images, audio, and video. The goal is to create models that can understand and make predictions based on more than one data modality, similar to how humans use various senses. This approach is used in applications like speech recognition with visual cues, image captioning, and video analysis. By combining different data types, multimodal learning systems can achieve better accuracy and more robust understanding.

What are the key skills and qualifications needed to thrive in multimodal learning, and why are they important?

To excel as a Multimodal Learning Specialist, you need a solid background in machine learning, data science, and computer vision, often supported by an advanced degree in a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience integrating data from diverse sources (e.g., text, audio, images), and knowledge of relevant algorithms are crucial. Strong problem-solving abilities, creativity, and effective collaboration are standout soft skills for this role. These competencies are vital for developing innovative models that can process and interpret complex, multi-source data to drive impactful AI solutions.

What are some common challenges faced by professionals working in multimodal learning roles, and how can they be addressed?

Professionals in multimodal learning frequently encounter challenges related to integrating and aligning data from multiple sources, such as text, images, audio, or video. Ensuring data quality and consistency across modalities can be complex, and developing models that effectively combine heterogeneous information often requires advanced technical skills and innovative thinking. Collaboration with domain experts and other data scientists is key to overcoming these obstacles, as is staying up to date with the latest research and tools in machine learning. Regular team meetings and cross-disciplinary workshops can help foster a collaborative environment and promote knowledge sharing.

What is the difference between Multimodal Learning vs Data Scientist?

AspectMultimodal LearningData Scientist
Required CredentialsAdvanced degrees in AI, Machine Learning, or Computer ScienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, AI development teams, academiaBusiness, tech companies, analytics teams
Industry UsageAI research, multimedia applications, roboticsData analysis, predictive modeling, business insights

Multimodal Learning focuses on developing AI models that process and integrate multiple data types like images, text, and audio. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve data and algorithms, Multimodal Learning is specialized in AI model development for complex data integration, whereas Data Scientists work broadly across data analysis and interpretation.

What are popular job titles related to Multimodal Learning jobs in Indianapolis, IN?

For Multimodal Learning jobs in Indianapolis, IN, the most frequently searched job titles are:

What job categories do people searching Multimodal Learning jobs in Indianapolis, IN look for?

The top searched job categories for Multimodal Learning jobs in Indianapolis, IN are:

What cities near Indianapolis, IN are hiring for Multimodal Learning jobs?

Cities near Indianapolis, IN with the most Multimodal Learning job openings:

Multimodal Learning jobs near you
Infographic showing various Multimodal Learning job openings in Indianapolis, IN as of August 2026, with employment types broken down into 57% Full Time, and 43% Contract. Highlights an 100% In-person job distribution, with an average salary of $58,969 per year, or $28.4 per hour.

Computational Biology MLOps Engineer (Remote)

Indianapolis, IN • Remote

Marlabs
IT Services • 1 - 5K employees
Learn more about Marlabsexternal

$114K - $134K/yr

Full-time

Re-posted 8 days ago

Job description

Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest healthcare, life sciences, financial services, and government organizations worldwide. As we continue to expand our global footprint, we have an exciting opportunity for a highly skilled Computational Biology MLOps Engineer to join our innovative and dynamic team.  

This role requires onsite work three (3) days per week in Indianapolis, IN or San Diego, CA. 

Computational Biology MLOps Engineer | About You 

As a Computational Biology MLOps Engineer, you are responsible for building and scaling the ML infrastructure that supports next generation in silico protein design and engineering. You bridge cutting edge AI research and production systems at the intersection of machine learning, computational biology, and high performance computing. You thrive in cross functional environments and partner closely with computational scientists and platform engineers to accelerate research velocity. You bring strong software, DevOps, and data engineering fundamentals with hands on experience across CI/CD, orchestration, and distributed training. Experience working with scientific or multimodal data and interest in protein language and generative models is a plus. 

Computational Biology MLOps Engineer | Day-to-Day 

  • Build and maintain ML infrastructure, including CI/CD pipelines (GitHub Actions) for model training, evaluation, and deployment. 
  • Orchestrate compute across Kubernetes clusters and SLURM and HPC environments to optimize utilization for large scale training. 
  • Develop robust and scalable data pipelines that deliver ML ready datasets from biological sources such as PDB and mmCIF files, sequence databases, and assay readouts. 
  • Create tools and frameworks that enable rapid iteration on protein language models, diffusion models, and other generative approaches. 
  • Architect systems that scale across distributed environments and support multimodal datasets for large foundational models. 
  • Implement monitoring, logging, and alerting to ensure reliability, performance, and cost efficiency of production ML systems. 

Computational Biology MLOps Engineer | Skills & Experience 

  • 5+ years of overall industry experience in software engineering, DevOps, data engineering, or ML engineering roles, including 3+ years of focused MLOps experience building and maintaining production grade ML infrastructure. 
  • Proven CI/CD expertise with GitHub Actions and strong DevOps practices including infrastructure as code, version control, and collaborative workflows. 
  • Hands on Kubernetes experience in deploying and managing containerized ML workloads with familiarity using container registries. 
  • Proficiency with SLURM or similar job schedulers in HPC environments and experience with distributed training optimization including mixed precision and checkpointing. 
  • Strong Python skills and experience with major ML frameworks including PyTorch, TensorFlow, or JAX. 
  • Experience building ETL processes and scalable data and feature pipelines and experience with cloud platforms such as AWS, GCP, or Azure. 
  • Preferred experience with scientific data, protein structure formats such as PDB and mmCIF, protein AI models including ESM, and agentic systems such as MCP, LangGraph, and LangChain.