2026 Intern – Algorithm Development for Enzyme Optimization

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The Position

The opportunity:

We are seeking a highly motivated summer intern to support the development of algorithms for enzyme optimization within Roche Diagnostics. You will work in a hybrid setting with a multidisciplinary team of computational scientists data scientists and protein engineers to design implement and evaluate methods that improve enzyme performance for diagnostic applications.

This role is ideal for a student who enjoys working at the intersection of advanced optimization techniques protein language models (PLMs) and molecular biology/protein engineering.

• Develop and prototype computational methods for enzyme optimization (e.g. sequence design mutational scanning in silico screening).

• Apply fine-tune and interpret protein language models (PLMs) to analyze and generate protein sequences.

• Design and implement optimization techniques (e.g. Bayesian optimization evolutionary strategies genetic algorithms gradient-based or heuristic methods) to guide sequence selection and experimental design.

• Analyze sequencefunction datasets from past and ongoing experiments; perform data preprocessing feature engineering and statistical analysis.

• Collaborate with Roche Diagnostics team members to translate biological questions into well-defined computational problems and optimization objectives.

• Document code analyses and models in a reproducible manner (e.g. Git notebooks internal reports).

• Present progress and findings to the team through brief presentations and written summaries.

Who you are:

• Currently pursuing a Masters or PhD in a relevant field (e.g. Computational Biology Bioinformatics Computer Science Data Science Biophysics Molecular Biology or related).

• Strong programming skills in Python and experience with scientific/ML libraries (e.g. NumPy pandas PyTorch/TensorFlow scikit-learn JAX).

• Good understanding of optimization techniques such as Bayesian optimization genetic algorithms/evolutionary strategies reinforcement learning for black-box optimization or related methods.

• Good understanding of protein language models (PLMs):Experience using or studying models such as ESM ProteinMPNN ProtTrans or similar sequence-based representation with sequence embeddings model fine-tuning or downstream tasks (e.g. fitness prediction stability/activity prediction).

• Foundational knowledge of molecular biology and protein engineering including concepts like mutagenesis enzyme activity stability and structurefunction relationships.

• Strong analytical and problem-solving skills with the ability to work autonomously between meetings in a hybrid environment.

• Good communication skills and willingness to collaborate with cross-functional international teams.

• Coursework or research experience in molecular modeling and computational structural biology.

• Familiarity with methods for molecular docking or protein-protein/protein-ligand interaction modeling.

Preferred Qualifications:

• Prior experience with protein engineering projects directed evolution or sequencefunction prediction models.

• Experience working with large biological sequence datasets and high-throughput screening or assay data.

• Familiarity with cloud computing environments (e.g. AWS GCP Azure) and/or GPU-accelerated training and inference.

• Experience using version control (Git/GitHub) and reproducible research tools (Jupyter conda/poetry Docker etc.).

• Coursework or research experience in probabilistic modeling optimization or machine learning for biological systems.

Additional Information:

Location: Hybrid Based in Mississauga (minimum 3 days in the office)

Hours: Full-time (35 hours per week) Internship

Length: This position is a 4 months internship (full-time 35 hours per week) and is expected to begin in May 2026.

This position is not eligible for relocation support.

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The expected salary range for this position based on the primary location of Mississauga isandof hiring range. Actual pay will be determined based on experience qualifications and other job-related factors as determined by the company.

We use artificial intelligence to screen assess or select applicants for this role.

This posting is for an existing vacancy at Hoffmann-La Roche Ltd.

Who we are

A healthier future drives us to innovate. Together more than 100000 employees across the globe are dedicated to advance science ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities foster creativity and keep our ambitions high so we can deliver life-changing healthcare solutions that make a global impact.

Lets build a healthier future together.