Co-op, Machine Learning for Digital Twins

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Early From the posting source checked Jun 20, 2026

Application

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Co-op, Machine Learning for Digital Twins Cambridge, MA USA 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

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