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Computational Engineering Jobs (NOW HIRING)

Kanak Elite Services Inc

AI Engineer

Santa Clara, CA · On-site

D. in Materials Science, Computational Engineering, AI/ML, or related field. Technical Expertise: * Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow). * Experience with generative ...

Goldbelt, Inc.

AI/Computational Biologist

Fort Detrick, MD · On-site

$155K - $165K/yr

Master's or PhD in Molecular, Computational or Structural Biology, Bioinformatics, Biomedical Engineering, Biophysics/Computational Chemistry, Pharmaceutical Science, Chemistry, Immunology, or a ...

NewLimit

Computational Biologist

South San Francisco, CA · On-site

$150K - $200K/yr

Description NewLimit is seeking a Computational Biologist to join our Predict team. In this role ... Strong software engineering fundamentals and experience working with bioinformatics tools and ...

Panthalassa

Senior Computational Engineer

Portland, OR

$110.80K - $152.20K/yr

About the Job As a Senior Computational Engineer, you will lead and improve the use of high ... The ideal candidate has deep marine CFD experience, strong first-principles engineering judgment ...

Takeda

Director, Computational Biology

Boston, MA · On-site +1

Collaborate with AI/ML, data science, and data engineering teams to build scalable analytical ... PhD in Computational Biology or a related discipline, plus 10+ track record of scientific ...

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How much do computational engineering jobs pay per year?

As of May 30, 2026, the average yearly pay for computational engineering in the United States is $121,515.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,500.00 and $131,500.00 per year, depending on experience, location, and employer.

What is a Computational Engineering job?

A Computational Engineering job involves using mathematical models, algorithms, and computer simulations to analyze and solve engineering problems. It combines principles from computer science, applied mathematics, and engineering to improve product design, optimize systems, and enhance efficiency in various industries. Professionals in this field develop software tools, conduct simulations, and utilize high-performance computing to solve complex engineering challenges in areas such as aerospace, automotive, energy, and healthcare.

What are the key skills and qualifications needed to thrive in the Computational Engineering position, and why are they important?

To thrive as a Computational Engineer, a strong background in mathematics, computer science, and engineering fundamentals is essential, generally supported by a degree in computational engineering or a related field. Familiarity with programming languages like Python, MATLAB, or C++, as well as experience using simulation software and high-performance computing systems, is typically required. Analytical thinking, effective communication, and problem-solving abilities are important soft skills for collaboration and innovation. These competencies enable Computational Engineers to develop accurate models, optimize complex systems, and deliver efficient solutions in multidisciplinary environments.

What are some common challenges Computational Engineers face in their work?

Computational Engineers often encounter complex, large-scale problems that require developing accurate and efficient computational models, which can be challenging due to intricacies in physical systems or computational resource limitations. Managing tight project deadlines while ensuring high-quality results and adapting to rapidly evolving technology are also common aspects of the role. Collaboration across multidisciplinary teams—often with scientists, designers, or other engineers—requires strong communication and adaptability. Embracing these challenges can help Computational Engineers expand their expertise and positively impact project outcomes.
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Computational Engineering jobs near you
Infographic showing various Computational Engineering job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 14% Full Time, 50% Part Time, and 35% Contract. Highlights an 96% Physical, and 4% Hybrid job distribution, with an average salary of $121,515 per year, or $58.4 per hour.
Computational Biologist

Computational Biologist

Verge Genomics

San Francisco, CA • On-site, Remote

Full-time

Posted 26 days ago

Job description

Who We Are
Verge is transforming drug discovery by using artificial intelligence and proprietary human data to solve the biggest driver of rising drug costs: high clinical failure rates. To achieve this, we have built one of the field's largest corpuses of multi-modal patient molecular and clinical data, sourced directly from human tissue. Our team of engineers, neuroscientists, and biologists have so far delivered two drugs to clinic, discovered 282 new targets, and signed commercial partnerships worth in excess of $1.6B with Eli Lily and AstraZeneca.
Your Mission
Reporting to the Head of Product & Engineering, and working alongside Verge's platform and computational biology teams, the Computational Biologist (AI/ML) will be responsible for defining and enabling new product offerings leveraging Verge's drug discovery engine for internal stakeholders, external partners (across both pharma and AI), and customers.
Your 12 Month Outcomes
  • Work with Verge's AI partners to deliver a best-in-class biology foundation model with Verge's proprietary datasets
  • Develop a novel approach that enables a powerful new product offering (patient stratification, biomarker discovery, etc.)
  • Deliver at least two CONVERGE-powered insights projects to pharma/biotech companies
  • Build an internal agentic AI workflow that supports multi-modal biomedical reasoning and orchestration

You Will
  • Develop and evaluate cutting-edge computational methodologies integrating multi-omic datasets to develop predictive models for translational biology,
  • Lead high-impact projects that apply and adapt AI models to translational challenges in disease biology, biomarker discovery, and target exploration,
  • Lead partnerships with AI companies to co-develop next-generation foundation models for drug discovery
  • Frame biological problems in computational terms and design solutions that are biologically meaningful, interpretable, and experimentally testable,
  • Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications,
  • Translate between biological domain knowledge and machine learning objectives.

Requirements
Candidates must have:
  • Either:
    • PhD in computational biology, AI/ML, applied statistics, biophysics, or,
    • MS and professional experience in relevant fields.
  • ≥5 years of experience working in applied computational biology and integration of multi-omic datasets (RNA-seq, genotyping, clinical), with ≥2 years in a startup environment,
  • ≥2 years of experience in relevant areas of translational science, demonstrating a deep understanding of target identification, biomarker discovery, and/or patient stratification,
  • Proven ability to implement, evaluate, and/or create computational methodologies that leverage machine learning, statistics, and AI for biological research and discovery,
  • Fluency with state of the art in systems biology workflows, including off-the-shelf biological databases and computational biology tools,
  • Track record of bridging biological domain knowledge with computational approaches to solve real scientific problems
  • Track record of individual innovation, with published research or shipped work influencing pharma R&D decisions
  • Experience running a significant number of end-to-end RNA-Seq data analyses (from QC, read quantification, normalization through to interpretation),
  • Excellent coding skills in Python, with experience in relevant ML/AI libraries (e.g., PyTorch, HuggingFace, scikit-learn, pandas, numpy). A demonstrable portfolio (e.g., GitHub, research code, or shared notebooks) is highly preferred,
  • Experience in building and evaluating machine learning models on biological data, ideally with transformer-based models (e.g., scGPT, Geneformer, ESM, ProtBERT), with a deep understanding of feature selection, model interpretability,
  • Professional experience with AI workflows, including natural language processing (NLP), retrieval-augmented generation (RAG), embeddings, vectorization of diverse data types, and working with large language models (e.g., GPT),
  • Demonstrated experience with model evaluation and experimental design in a scientific context, including setting up appropriate benchmarks and controls.

Finally, we seek candidates who embrace our values and way of working:
  • Ability to thrive in uncertainty with frequently changing priorities
  • Deep alignment with our values
  • A passion for making an impact on patients

Apply