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Scientific Computing Jobs in Boston, MA (NOW HIRING)

QuEra Computing, Inc.

Optical Scientist - R&D

Boston, MA · On-site

$130K - $211K/yr

Summary QuEra Computing is pioneering the future of quantum computing technology. We are seeking an Optical Scientist to join our growing team and help create the world's largest and first ...

Quantiphi, Inc.

Client Partner

Boston, MA · On-site

Identify new opportunities and expand existing engagements across AI, data, cloud, analytics, digital engineering, and scientific computing. * Partner with Quantiphi's AI, engineering, and Life ...

Quantiphi

Client Partner

Boston, MA · On-site

Identify new opportunities and expand existing engagements across AI, data, cloud, analytics, digital engineering, and scientific computing. * Partner with Quantiphi's AI, engineering, and Life ...

QuEra Computing, Inc.

Optical Scientist - R&D

Boston, MA · On-site

$130K - $211K/yr

Summary QuEra Computing is pioneering the future of quantum computing technology. We are seeking an Optical Scientist to join our growing team and help create the world's largest and first ...

Quantiphi, Inc.

Client Partner

Boston, MA · On-site

Identify new opportunities and expand existing engagements across AI, data, cloud, analytics, digital engineering, and scientific computing. * Partner with Quantiphi's AI, engineering, and Life ...

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Scientific Computing information

See Boston, MA salary details

$15

$34

$56

How much do scientific computing jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for scientific computing in Boston, MA is $34.20, according to ZipRecruiter salary data. Most workers in this role earn between $20.91 and $43.61 per hour, depending on experience, location, and employer.

What does someone working in scientific computing do?

Professionals in Scientific Computing typically spend their days developing and optimizing computational models, writing code to analyze large datasets, and running simulations on high-performance computing systems. They often collaborate closely with scientists, researchers, or engineers to interpret results and improve methodologies. Depending on the industry, they may also be responsible for documenting workflows, troubleshooting complex issues, and staying current with technological advances in their field. Daily work often involves problem-solving, technical meetings, and the continuous improvement of algorithms and computational processes.

What are the key skills and qualifications needed to thrive in scientific computing?

To thrive in Scientific Computing, you need a strong background in mathematics, computer science, and scientific principles, often supported by a relevant degree such as physics, engineering, or computational science. Proficiency with programming languages like Python, C++, or MATLAB, experience with high-performance computing (HPC) systems, and familiarity with scientific software and libraries are typically essential. Excellent problem-solving abilities, teamwork, and clear communication skills are important soft skills for this role. These skills enable professionals to develop efficient computational solutions, collaborate effectively across multidisciplinary teams, and drive progress in research and innovation.

What is scientific computing?

A Scientific Computing job involves using advanced computational methods, algorithms, and mathematical modeling to solve complex scientific and engineering problems. Professionals in this field develop and optimize software, perform simulations, and analyze large datasets to support research in disciplines like physics, biology, and engineering. They often work with high-performance computing (HPC) systems and programming languages such as Python, C++, or Fortran. These roles are commonly found in academia, government research labs, and industries like aerospace, pharmaceuticals, and finance.

What are the most commonly searched types of Scientific Computing jobs in Boston, MA? The most popular types of Scientific Computing jobs in Boston, MA are:
What are popular job titles related to Scientific Computing jobs in Boston, MA? For Scientific Computing jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Scientific Computing jobs in Boston, MA look for? The top searched job categories for Scientific Computing jobs in Boston, MA are:
Scientific Computing jobs near you
Infographic showing various Scientific Computing job openings in Boston, MA as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 79% Full Time, 14% Part Time, 1% Temporary, and 4% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution, with an average salary of $71,126 per year, or $34.2 per hour.

Co-op, Machine Learning for Digital Twins

Lila Sciences

Cambridge, MA • On-site, Remote

Other

Re-posted 28 days ago

Job description

Your Impact at LILA

Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, our team partners with the diverse experimental groups to build digital twins of experimental campaigns, focusing on calibrated, uncertainty-aware models that enable higher-throughput, higher-quality use of Lila's AI Science Facilities (AISF).

As an ML for Digital Twins Co-Op, you will work on building, training, and evaluating ML models for physical and experimental systems. You will get hands-on experience with operator learning, surrogate modeling, and uncertainty quantification, shipping work that directly informs how next-generation AISF experiments are designed and run.

What You'll Be Building

  • Contribute to ML models for scientific and experimental systems, focused on a well-defined digital twin sub-problem
  • Build and train surrogate, operator-learning, or physics-informed models against experimental and simulation data, with mentor guidance
  • Calibrate models, quantify uncertainty, and validate against data flowing from active AISF experimental campaigns
  • Frame open-ended scientific questions as concrete ML tasks with clear datasets, baselines, and evaluation criteria
  • Document findings and share results in cross-departmental collaboration through write-ups and presentations

What You'll Need to Succeed

  • Pursuing a Master's or PhD in Machine Learning, Computer Science, Applied Mathematics, Physics, Materials Science, Chemical Engineering, Mechanical Engineering, Electrical Engineering, or a related quantitative field (PhD preferred)
  • Strong programming skills in Python and hands-on experience with ML frameworks such as PyTorch, JAX, TensorFlow, or similar
  • Experience applying machine learning to scientific, engineering, physical, or experimental systems
  • Familiarity with neural operators, operator learning, spatiotemporal modeling, field prediction, dynamical systems, scientific computing, surrogate modeling, or physics-informed ML
  • Ability to turn open-ended scientific questions into concrete ML tasks with clear datasets, assumptions, baselines, and evaluation criteria
  • Solid foundation in model training, validation, debugging, experiment tracking, and performance evaluation
  • Comfort working with messy, heterogeneous, or evolving scientific datasets
  • Clear communication and interest in collaborating across ML, software engineering, and physical science teams

Bonus Points For

  • Experience with modern operator-learning methods, including Fourier Neural Operators, DeepONets, graph neural operators, transformer-based neural operators, attention-based operators, physics-informed operators, or operator learning for spatiotemporal systems
  • Experience with digital twins, model update, calibration, and uncertainty-aware scientific modeling, including online/offline model updating, simulator calibration, discrepancy modeling, uncertainty quantification, out-of-distribution detection, or reliability estimation
  • Experience with closed-loop scientific decision-making or physical science applications, including active learning, Bayesian optimization, design of experiments, experimental decision-making, or applications in materials science, chemistry, energy systems, catalysis, batteries, electrochemistry, additive manufacturing, fluid dynamics, thermodynamics, robotics, or computational physics

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