Mass Spectrometry Scientist / Postdoctoral Researcher

Native MS, Structural Proteomics & Protein Complexes
Location: Novato, CA (on-site)

Overview
The laboratory of Dr. Kai Zhou at the Buck Institute for Research on Aging seeks a hands-on mass spectrometry scientist or postdoctoral researcher to establish and operate a structural mass spectrometry platform. We welcome candidates with expertise in native mass spectrometry, crosslinking mass spectrometry, HDX-MS, or intact-protein/top-down analysis.

Our laboratory investigates how protein structure, interactions, and assembly change with aging and disease. Projects include intrinsically disordered proteins (IDR/IDPs), protein complexes, and ribosomes.

The successful candidate will take substantial ownership of instrument operation, method development, data quality, and biological interpretation. The role offers opportunities to develop new workflows and pursue scientific questions in collaboration with protein biochemists, structural biologists, and computational researchers.

Responsibilities

• Establish, optimize, and run structural-MS workflows, including low-flow LC for bottom-up proteomics and crosslinking, and a separate LC workflow for intact-protein and native MS analysis.
• Develop native-MS and/or crosslinking-MS methods to characterize protein-complex composition, stoichiometry, interactions, and structural changes. Explore intact-complex analysis and complex-down MS workflows where experimentally feasible.
• Prepare and evaluate samples, including buffer exchange, desalting, crosslinking, digestion, and enrichment as appropriate; optimize native SEC or rapid online buffer exchange while assessing sample integrity and complex stability.
• Optimize ionization, ion transmission, precursor isolation, and fragmentation for heterogeneous proteins and assemblies; assess instrument performance, troubleshoot problems, and coordinate maintenance and training with vendor specialists.
• Establish quality-control standards, reproducible protocols, and reliable workflows for increasing sample throughput, including automated or unattended acquisition, batch QC, carryover checks, and reproducible switching between native and denaturing analyses.
• Analyze and interpret spectra with appropriate controls, uncertainty assessment, and validation; distinguish solution-state interactions from source-induced dissociation or adducts and assess crosslink identifications and proteoform/PTM assignments as appropriate.
• Maintain clear experimental records and organize raw data, analysis files, and protocols.
• Collaborate on experimental design, train colleagues, and contribute to publications and consortium deliverables.

Qualifications
Required

• PhD in analytical chemistry, biochemistry, biophysics, structural biology, or a related discipline. Experienced master’s-level applicants with substantial independent instrument-operation and method-development experience may be considered for an appropriate staff appointment.
• Demonstrated hands-on operation of a high-resolution mass spectrometer, including acquisition-method setup, performance assessment, and troubleshooting.
• Substantial practical expertise in at least one of the following: native MS, crosslinking MS, HDX-MS, or intact-protein/top-down MS.
• Experience connecting sample preparation, acquisition settings, data analysis, and experimental controls to reliable conclusions.
• Strong scientific reasoning, careful documentation, and the ability to work independently and collaboratively.

Preferred

• Native electrospray, native MS, intact-protein/top-down or complex-down analysis, or multistage fragmentation.
• Crosslinker selection, reaction optimization, enrichment, crosslinked-peptide identification, and false-discovery-rate assessment.
• Recombinant protein purification, sample-integrity assessment, and troubleshooting aggregation or heterogeneity.
• Analysis of disordered proteins, heterogeneous assemblies, or ribonucleoprotein complexes.
• Workflow automation, quantitative analysis, or scripting for reproducible data processing; familiarity with crosslinking or proteoform-analysis software. Experience supporting complementary bottom-up proteomics or metabolomics applications is also welcome.

Candidates need not have experience in every listed technique. We value demonstrated depth, ownership, and the ability to learn complementary approaches.

Compensation, Benefits & Additional Information

• Annual salary range: $75,920 - $85,500, commensurate with qualifications and experience.
• Generous benefits package including health insurance, paid parental leave, vacation/sick, and 401(k) with 5% employer match.
• Visa sponsorship (J visa) is available for this position.

About the Buck Institute
Our success will ultimately change healthcare. At the Buck, we aim to end the threat of age-related diseases for this and future generations by bringing together the most capable and passionate scientists from a broad range of disciplines to identify and impede how we age. We are an independent, non-profit institution located in Marin County, CA, with the goal of increasing human healthspan, or the healthy years of life. Globally recognized as the pioneer and leader in efforts to target aging—the number one risk factor for diseases including Alzheimer’s, Parkinson’s, cancer, macular degeneration, heart disease, and diabetes—the Buck seeks to help people live better longer.

We are an equal-opportunity employer and strive to create an atmosphere where diversity of identity, experience, and background is welcomed, valued, and supported. Candidates who contribute to this diversity are strongly encouraged to apply.

To Apply

• Please submit a CV, a brief cover letter, and contact information for three references.
• Postdoctoral applicants, please include your completed or expected PhD date.
• In your cover letter, describe one representative MS project and specify:
  • The instrument and techniques you used.
  • Which sample-preparation, acquisition, troubleshooting, and analysis steps you personally performed.
  • A technical problem you solved and how you verified the solution.
  • Your scientific contribution and the resulting publication, method, dataset, or operational improvement.