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Computational Engineer Jobs in New York (NOW HIRING)

Position Summary We are seeking a Computational Biologist who is passionate about using data-driven ... Expertise in data engineering and reproducible research tools (e.g., Docker, Nextflow, Snakemake ...

Position Summary We are seeking a Computational Biologist who is passionate about using data-driven ... Expertise in data engineering and reproducible research tools (e.g., Docker, Nextflow, Snakemake ...

Thea Energy is leveraging recent breakthroughs in stellarator physics and engineering to create a ... We are seeking highly motivated Computational Plasma Physicists to join our team, focusing on ...

Computational Plasma Physicist

Kearny, NJ · On-site

$120K - $140K/yr

Thea Energy is leveraging recent breakthroughs in stellarator physics and engineering to create a ... We are seeking highly motivated Computational Plasma Physicists to join our team, focusing on ...

Computational Designer

New York, NY · On-site

$115K - $143K/yr

EDEN is a digital design environment for the engineering and design of ecosystems , modeling the ... Role Overview OXMAN is seeking a Computational Designer to join the EDEN design team and develop ...

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Computational Engineer information

See New York salary details

$53.1K

$132.9K

$150.4K

How much do computational engineer jobs pay per year?

As of Aug 6, 2026, the average yearly pay for computational engineer in New York is $132,942.00, according to ZipRecruiter salary data. Most workers in this role earn between $122,000.00 and $143,900.00 per year, depending on experience, location, and employer.

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

To thrive as a Computational Engineer, you need a strong background in mathematics, physics, computer science, and engineering principles, typically supported by a relevant degree. Proficiency in programming languages (such as Python, C++, or MATLAB), experience with simulation software (like ANSYS or COMSOL), and familiarity with high-performance computing environments are essential. Strong analytical thinking, problem-solving abilities, and effective communication skills help set top performers apart in this field. These skills are crucial for developing accurate models, optimizing complex systems, and effectively collaborating in multidisciplinary teams.

What is a computational engineer?

A computational engineer is a professional who uses advanced computing techniques and mathematical models to solve complex engineering problems. They develop simulations, algorithms, and software to analyze and optimize systems in fields such as aerospace, automotive, civil engineering, and more. Their work often involves programming, data analysis, and applying principles from physics and mathematics to create efficient solutions. Computational engineers bridge the gap between traditional engineering and computer science, enabling innovation through technology.

How does a computational engineer typically collaborate with multidisciplinary teams on complex projects?

Computational Engineers frequently work alongside professionals from various disciplines such as mechanical engineers, data scientists, and software developers. Effective collaboration often involves translating engineering problems into computational models, communicating technical requirements, and integrating simulation results into broader project workflows. Regular meetings, shared documentation, and collaborative software tools are commonly used to ensure alignment and progress. This team-based approach helps deliver accurate and actionable insights for product design, optimization, or research objectives.

What do computational engineers do?

Computational engineers develop and apply mathematical models, algorithms, and simulations to solve complex engineering problems. They often use programming languages and software tools to analyze data, optimize systems, and support design processes across various industries such as aerospace, automotive, and energy.

How much do computational engineers make?

Computational engineers typically earn a median salary ranging from $80,000 to $120,000 annually, depending on experience, education, and industry. Senior roles or those with specialized skills in programming, simulation, or data analysis can command higher salaries, especially in high-demand sectors like aerospace, defense, or technology companies.

What is the difference between Computational Engineer vs Software Engineer?

AspectComputational EngineerSoftware Engineer
Required CredentialsBachelor's or Master's in Engineering, Computer Science, or related fields; knowledge of programming and numerical methodsBachelor's or Master's in Computer Science, Software Engineering, or related fields; strong programming skills
Work EnvironmentResearch labs, engineering firms, tech companies; often involves simulation and modelingTech companies, startups, software firms; focuses on application development and system design
Industry UsageEngineering, aerospace, automotive, scientific researchIT, software development, technology services

Computational Engineers and Software Engineers share programming skills and work in tech-related environments. However, Computational Engineers focus more on applying computational methods to engineering problems, simulations, and modeling, while Software Engineers primarily develop software applications and systems. Both roles require strong technical backgrounds but serve different industry needs.

Infographic showing various Computational Engineer job openings in New York as of July 2026, with employment types broken down into 90% Full Time, 6% Part Time, and 4% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution, with an average salary of $132,942 per year, or $63.9 per hour.

Computational Biologist

Neptune Bio

New York, NY

Other

Re-posted 5 days ago


Job description

Position Summary

We are seeking a Computational Biologist who is passionate about using data-driven, scalable methods to reveal biological insights. The ideal candidate is an independent thinker with strong computational and quantitative skills, and the ability to collaborate closely with both experimental and computational scientists. You will design, implement, and scale computational pipelines for single-cell perturbation datasets, while contributing to model development and experimental design.

This is a unique opportunity to join a dynamic, interdisciplinary environment and help shape Neptune Bio's computational strategy and infrastructure.

Key Responsibilities

  • Develop, innovate, and maintain advanced computational methods to process, analyze, and interpret large-scale single-cell genomics and perturbation datasets.
  • Collaborate with wet-lab and computational teams to integrate data from diverse experimental modalities and guide experimental design.
  • Build, optimize, and scale data analysis pipelines using modern cloud computing environments (e.g., AWS, GCP, Azure).
  • Contribute to Neptune Bio's data infrastructure, ensuring reproducibility, scalability, and efficient access to large datasets.
  • Stay current with advances in computational biology, machine learning, and scalable infrastructure, applying them to ongoing research challenges.
  • Communicate findings clearly through reports, visualizations, and presentations to multidisciplinary audiences.

Qualification and Education Requirements

You must have:

  • Ph.D. in Bioinformatics, Computational Biology, Computer Science, or a related quantitative field, OR equivalent experience (e.g., BS/MS with 3 years of relevant experience).
  • Proficiency in Python, R, and Unix/Linux environments
  • Demonstrated experience in single-cell or multi-omics data analysis.
  • Solid understanding of statistics, data modeling, and modern machine learning approaches.
  • Experience deploying and scaling computational pipelines on cloud platforms (AWS, GCP, or similar).
  • Strong communication skills and enthusiasm for working in a collaborative, fast-paced environment.

Additional preferred experience includes:

  • Background in functional genomics, CRISPR screens, or perturb-seq analysis.
  • Experience integrating multi-source data to derive novel and impactful insights.
  • Expertise in data engineering and reproducible research tools (e.g., Docker, Nextflow, Snakemake) as well as familiarity with cloud-native architectures and distributed compute.
  • Strong publication record demonstrating innovation in computational methods or biological data analysis.
  • Experience with deep learning frameworks such as PyTorch or TensorFlow.