Bioinformatics Engineer

Arcus is seeking a Bioinformatics Engineer that will work in a highly embedded, collaborative model with colleagues across the organization.

The engineer will join highly technical, interdisciplinary teams and bring a creative, problem-solving mindset to challenging work. The successful applicant will help improve and harmonize bioinformatics processes, as well as take lead in augmenting a growing internal database of multimodal, high-dimensional datasets to enable critical bioinformatics functions. They will use their expertise to build infrastructure, develop pipelines and frameworks, optimize algorithms, and process harmonized high-dimensional molecular datasets.

The individual may also support cross-departmental infrastructure developments to enable effective inter-departmental information exchange and develop LLM systems (e.g., literature review agents, retrieval-augmented knowledge systems) to enable bioinformatics and drug development discoveries.

Responsibilities

• Process and QC high-dimensional datasets to expand internal databases (e.g., RNA-seq, scRNA-seq, spatial transcriptomics, proteomics, etc.).
• Maintain and improve internal pipelines for data processing, including RNA-seq and WES.
• Develop standardized internal pipelines for processing of additional data types such as scRNA-seq and spatial transcriptomics.
• Work closely with Bioinformatics Scientists on improving internal processes and databases to enable efficient data extraction and transformation for rapid bioinformatics analysis and visualization.
• Collaborate with constituencies in IT and Scientific Computing to improve on-prem and cloud-based infrastructures to support bioinformatics functions.
• Serve as the technical resource for optimizing pipelines, benchmarking algorithms, developing reproducible frameworks, and updating SOP documentation & internal knowledge base.

Qualifications

• Master’s in Bioinformatics, Computational Biology, Data Science, Computer Science, or similar field, with 5+ years of experience.
• Strong understanding of data structures and transformations to enable efficient modeling.
• Proven coding expertise in Unix and SQL for systems and database query and development.
• Proficiency in R and/or Python for bioinformatics pipelines and analysis.
• Experience with a workflow language such as Snakemake or Nextflow.
• Adept at building reproducible computing environments using tools such as Docker, Singularity/Apptainer, uv, or conda.
• Ability to work with on-prem and cloud-based systems including HPC clusters (e.g., SLURM) and AWS.
• Fluent with version control using Git/GitHub and collaborating with other bioinformatics scientists/engineers.
• Comfortable working in a cross-functional environment and handling several concurrent fast paced projects.

Preferred Experience

• Understanding of high-dimensional molecular data types, such as RNA-seq, scRNA-seq, etc.
• Familiarity with best coding practices such as unit testing, CI/CD, reproducible code, and modular pipeline design.
• Experience developing pipelines and general frameworks for data analysis.
• Ability to write concise technical documentation.
• Familiarity with LLM-based tools, including LLM-assisted and agentic programming.