Senior Associate Scientist – Proteogenomics & Immunopeptidomics Full-time NEW

Experienced – Senior Associate Scientist, Proteogenomics & Immunopeptidomics

Mass Spectrometry–Based Discovery of Novel Therapeutic Antigens

Build our future together:

We are seeking a motivated Senior Associate Scientist to join a translational proteomics group that discovers and validates novel cell-surface and HLA-presented antigens for therapeutic antibody, bispecific and cell-therapy programs. You will work at the interface of proteogenomics and immunopeptidomics, generating high-sensitivity LC-MS/MS data and helping turn peptide-level evidence into validated target candidates that discovery teams act on.

This is a hands-on laboratory role, and it is deliberately written as a role you can grow into. We do not expect candidates to arrive already expert in immunopeptidomics or proteogenomics — these are specialized techniques that very few people have trained in, and we would rather teach them to the right person. What we are looking for is a solid foundation in laboratory science, real care at the bench, and genuine appetite to learn mass spectrometry properly. Training is provided directly by experienced scientists in the group, on the instruments, alongside the actual project work.

A master's degree is an excellent fit for this role, and we welcome strong bachelor's-level candidates with relevant laboratory experience. Demonstrated hands-on capability and trajectory matter more here than degree level, and there is a clear path to progress as your technical independence grows.

Modern proteomics is part laboratory science and part data science, and our group uses both. Routine work increasingly involves a little code — a short Python or R script to reformat a sequence file, filter a long results table, or draw a figure — and the field's analysis software now leans heavily on machine learning, from deep-learning tools that predict peptide spectra and retention times to models that help rescore search results and improve confidence in an identification. AI coding assistants are also a normal part of how analyses get prototyped here.

None of this is a requirement for this position. We are hiring primarily for laboratory capability, and a strong candidate with no coding background at all is a strong candidate. We mention it because it is a genuine opportunity: if you are curious about the computational side, there is real scope to learn it here, with support, on live data — and scientists who develop that fluency alongside solid bench skills become unusually effective. If that holds no appeal, the role still works; the bench work is the core of it either way.

What you'll learn here:

Structured, hands-on training is part of the role rather than an afterthought. Over your first year you can expect to be trained in:

• Operation, calibration, routine maintenance and first-line troubleshooting of high-resolution mass spectrometers (Thermo Orbitrap and Bruker timsTOF platforms).
• Nanoflow liquid chromatography — column and emitter handling, diagnosing sensitivity loss, and keeping a demanding method stable.
• HLA class I and class II immunoprecipitation workflows, including low-input and limited-material samples.
• Database searching and the interpretation of proteomics results, including false-discovery control and how to tell a real identification from an artifact.
• Targeted quantitative assays (PRM/MRM) using stable isotope-labeled peptide standards.

Discover your role:

• Perform bottom-up proteomics sample preparation — cell and tissue lysis, enzymatic digestion, peptide cleanup, enrichment and fractionation — with the precision that low-input samples demand.
• Carry out immunoprecipitation and affinity-enrichment workflows, initially under guidance and progressing to independent execution.
• Maintain careful sample tracking, run records, reagent logs and quality-control practice so that datasets remain interpretable and auditable months later.
• Support general laboratory operations — reagent preparation, inventory, instrument housekeeping and safety compliance.
• Acquire LC-MS/MS data on high-resolution instrumentation, initially with supervision and increasingly on your own.
• Learn and then perform routine instrument maintenance, calibration, performance checks and first-line troubleshooting.
• Contribute to method optimization as your understanding of the platform develops, particularly for low-abundance analytes.
• Process MS data through established database search workflows and learn to apply appropriate false-discovery control.
• Review, filter and summarize results, and build the judgment to recognize when an identification is not well supported.
• Help search MS data against custom protein sequence databases derived from RNA-seq, ribosome profiling and long-read transcriptome data, and learn how those databases are built and curated.
• Opportunity — not expectation — to take on scripted analysis in Python or R as your interest and experience develop.
• Present your data clearly at group meetings, and contribute to project-team discussions as you build confidence.
• Work effectively with colleagues across proteomics, computational biology, genomics and bioinformatics.
• Keep clear written records and contribute to internal reports, with opportunities to contribute to manuscripts and external presentations.
• Ask questions early and often — in this group, raising a problem promptly is valued more highly than appearing to have everything under control.

This role requires:

• Education and experience: MS in analytical chemistry, biochemistry, molecular biology, biology, bioinformatics or a related discipline with approximately 2–4 years of relevant laboratory experience, or a BS in a relevant discipline with approximately 4–6 years of laboratory experience. Relevant graduate research, co-op or internship experience counts toward these ranges.
• Laboratory fundamentals: Solid, demonstrable wet-lab technique — accurate pipetting and solution preparation, aseptic and contamination-aware practice, and the discipline to follow a protocol exactly and record what you actually did.
• Biochemistry or analytical foundation: Coursework or practical experience in protein biochemistry, analytical chemistry or molecular biology sufficient to understand what the workflows are doing and why.
• Basic data literacy: Comfort working with large data files and spreadsheets, and the ability to keep organized, well-documented records. Programming is not required.
• Attention to detail: A track record of careful, reproducible work. Much of this science fails quietly on small handling errors, so this matters more than any single technique on a CV.
• Communication and teamwork: Ability to explain what you did and what you observed, and to work productively in a collaborative group.
• Preferred: Any hands-on LC-MS or mass spectrometry experience, in any application area — proteomics, metabolomics, small molecule or analytical QC.
• Preferred: Protein biochemistry technique — immunoprecipitation, affinity purification, Western blot, cell culture or tissue handling.
• Preferred: Bottom-up proteomics sample preparation, including digestion and peptide cleanup.
• Preferred: Exposure to proteomics data analysis software (e.g. PEAKS, FragPipe, Proteome Discoverer, Spectronaut).
• Preferred: Any immunology background, particularly HLA/MHC antigen presentation.
• Preferred: Any scripting in Python or R, however informal — a script written for a class, a thesis or a previous job all count. Depth is not expected.
• Preferred: Curiosity about machine learning applied to scientific data, or about reproducible analysis pipelines (e.g. Nextflow, nf-core, containerized workflows). Interest is welcome; experience is a bonus, not a filter.
• Preferred: Prior experience in a biotech, pharmaceutical, CRO, academic core-facility or university research laboratory setting.

What distinguishes a strong candidate:

• Careful hands. Much of this work succeeds or fails on sample-preparation technique and instrument condition before any analysis begins — so precision and consistency matter more than speed.
• Real appetite to learn the instrument. The people who do well here are the ones who want to understand why the method works, not only how to run it.
• Honesty about what went wrong. We would far rather hear that a sample was mishandled than discover it three weeks later in the data.
• Curiosity about the biology. The techniques are a means to finding targets that may become medicines; the best colleagues care about that end.
• Initiative and follow-through — noticing something is off, and chasing it down rather than waiting to be asked.
• Comfort with ambiguity. Much of this work targets sequences absent from any reference database, where the right approach is not yet settled.