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Roche

2027 Summer Intern - Computational Science

Roche is hiring a 2027 Summer Intern - Computational Science in Santa Clara, United States. It pays $47-$52/hr and Level rates it ; you can apply on Roche.

Level's read on this job

  • Level rates this role Little AI ●○○○: aI is not part of the work.
  • Level classes it as an internship.
  • Roche has 1,200+ other open roles on Level.
  • Posted in the last few days.

AI in this role

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

The Position

2027 Summer Intern - Computational Science


We advance science so that we all have more time with the people we love.


Department Summary


The XOOS Algorithm Development team leads secondary analysis algorithm development for Sequencing by Expansion (SBX) data on the AXELIOS platform. We bridge rapid R&D prototyping with production-grade bioinformatics tools, driving multi-omics whole genome and targeted sequencing applications across oncology, genetic disease, and infectious disease.


This internship position is fully remote.


The Opportunity

  • Algorithm Development.
  • Benchmarking.
  • Software development.
  • Presenting results to stakeholders.

Program Highlights

  • Intensive 12-weeks, full-time (40 hours per week) paid internship.
  • Program start dates are in May/June (Summer).
  • A stipend, based on location, will be provided to help alleviate costs associated with the internship.
  • Ownership of challenging and impactful business-critical projects.
  • Work with some of the most talented people in the biotechnology industry.

Who You Are


Required Education:

  • Must be pursuing a PhD (enrolled student).

Required Majors: Bioinformatics, Computer Science, Computational Biology.


Required Skills

  • Advanced experience developing bioinformatics algorithms for secondary analysis of sequencing data.
  • Demonstrated expertise in detecting genetic variation in challenging genomic contexts, including repeat regions, paralogs, pseudogenes, and highly homologous loci.
  • Strong understanding of haplotype phasing, read alignment, local or de novo assembly, scaffolding, and pangenome methods.
  • Experience developing tools for one or more of the following:Repeat expansion or repeat-genotype analysis, Paralog- or pseudogene-aware variant calling, Long-read or highly accurate sequencing analysis, Haplotype reconstruction and phasing, De novo or targeted assembly, Graph genomes or pangenome analysis.
  • Strong programming skills in Python and/or C++, with experience building reproducible, tested software.
  • Experience evaluating algorithms with representative datasets and quantitative performance metrics.
  • Currently enrolled in or recently graduated from a degree program in bioinformatics, computational biology, computer science, statistics, mathematics, genomics, or a related field.

Preferred Knowledge, Skills, and Qualifications

  • Demonstrated record of publications, preprints, or equivalent technical contributions describing novel algorithms or methods for variant detection and genotyping.
  • Research experience focused on difficult-to-map regions of the genome, including repeats, paralogs, pseudogenes, segmental duplications, and other highly homologous loci.
  • Publications or documented contributions involving one or more of the following:Repeat-variant detection or genotyping, Haplotype phasing and reconstructionLocal, targeted, or de novo assembly, Scaffolding and contig generation, Pangenome or graph-based analysisLong-read sequencing analysis, Paralog- or pseudogene-aware variant calling.
  • Ability to explain algorithmic innovations, validation methodology, limitations, and performance results clearly.
  • Evidence of translating research methods into reproducible prototypes or usable bioinformatics tools.

Relocation benefits are not available for this job posting.


The expected salary range for this position based on the primary location of California is $47.00-$52.00 per hour. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off benefits.

Who we are

A healthier future drives us to innovate. Together, more than 100’000 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.


Let’s build a healthier future, together.

Roche is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.

How we rate this

2027 Summer Intern - Computational Science at Roche rates 24 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

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