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Computational Biology Jobs in Indiana (NOW HIRING)

Vivum AI

Computational Neuroethologist

Bloomington, IN · On-site

Responsibilities : • Study and map cognitive and biological neural networks. • Contribute ... computational modeling Company : The Biological Intelligence Company for Scalable AI Founded in ...

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Computational Biology information

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$46.2K

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How much do computational biology jobs pay per year?

As of Sep 6, 2026, the average yearly pay for computational biology in Indiana is $89,436.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,100.00 and $111,300.00 per year, depending on experience, location, and employer.

What is computational biology?

Computational biology is an interdisciplinary field that uses data analysis, mathematical modeling, and computer simulations to understand biological systems and relationships. Researchers in this area develop algorithms and software to analyze large sets of biological data, such as DNA sequences or protein structures. Computational biology plays a crucial role in genomics, drug discovery, systems biology, and personalized medicine, helping scientists make sense of complex biological information.

What are the key skills and qualifications needed to thrive as a computational biologist, and why are they important?

To thrive as a Computational Biologist, you need a strong background in biology, statistics, and computer science, often supported by a relevant degree such as bioinformatics, computational biology, or a related discipline. Familiarity with programming languages like Python or R, experience using bioinformatics tools (e.g., BLAST, Bioconductor), and knowledge of data analysis pipelines are typically required. Strong problem-solving, collaboration, and communication skills help computational biologists interpret complex datasets and work effectively with interdisciplinary teams. These competencies are crucial for extracting meaningful biological insights from large datasets and advancing research in genomics, drug discovery, and personalized medicine.

How do computational biologists typically collaborate with experimental scientists in research projects?

Computational biologists frequently work alongside experimental biologists to interpret data, design experiments, and develop new hypotheses. Collaboration often involves regular meetings to align on research goals, data sharing, and troubleshooting analytical challenges together. Being able to communicate complex computational findings in accessible terms is crucial for ensuring that experimental teams can act on the insights provided. This interdisciplinary teamwork not only enhances research outcomes but also broadens professional skill sets, making the role both dynamic and rewarding.

What is the difference between Computational Biology vs Bioinformatics?

AspectComputational BiologyBioinformatics
Required CredentialsTypically requires a PhD in biology, bioinformatics, or related fieldsOften requires a bachelor's or master's degree in computer science, biology, or bioinformatics
Work EnvironmentResearch labs, academia, biotech companiesResearch labs, healthcare, biotech, and pharmaceutical industries
Industry UsageUsed for modeling biological systems and understanding complex biological dataPrimarily focused on developing algorithms and tools to analyze biological data

Computational Biology and Bioinformatics are closely related fields that often overlap. Computational Biology emphasizes modeling and understanding biological systems through computational methods, often requiring advanced degrees. Bioinformatics focuses on developing tools and algorithms to analyze biological data, typically with a background in computer science or biology. Both roles are vital in research and industry, but they differ in their primary focus and educational requirements.

What can you do with a computational biology degree?

A computational biology degree prepares individuals for roles such as bioinformatics analyst, research scientist, or data scientist in healthcare, biotech, and pharmaceutical industries. Graduates use skills in programming, statistical analysis, and biological data interpretation to develop models, analyze genomic data, and support drug discovery. Proficiency in tools like Python, R, and databases is often required.

What are the most commonly searched types of Computational Biology jobs in Indiana?

The most popular types of Computational Biology jobs in Indiana are:

What are popular job titles related to Computational Biology jobs in Indiana?

For Computational Biology jobs in Indiana, the most frequently searched job titles are:

What job categories do people searching Computational Biology jobs in Indiana look for?

The top searched job categories for Computational Biology jobs in Indiana are:

Computational Biology jobs near you
Infographic showing various Computational Biology job openings in Indiana as of August 2026, with employment types broken down into 64% Full Time, 32% Part Time, 1% Temporary, and 3% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $89,436 per year, or $43 per hour.

Computational Biology MLOps Engineer

MarLabs

Indianapolis, IN • On-site

$140 - $180/hr

Other

Posted 5 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.
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