Senior Scientist I, Proteomics Platform

Generate Biomedicines•Somerville, MA

1d•$125,000 - $175,000

About The Position

The Role: Generate: Biomedicines is seeking an experienced and highly motivated Senior Scientist, Proteomics Platform to join our team at the interface of proteomics, molecular biology, ML, and biopharmaceutical discovery. This role will be central to reimagining and building proteomics capabilities for large-scale data generation and high-throughput screening, using a rapid prototyping mindset to construct tools, workflows, and capabilities across the full proteomics pipeline. The ideal candidate will bring deep technical expertise spanning the full proteomics workflow, from sample preparation and LC-MS/MS method development through data processing, interpretation, and cross-study analysis. This person will have a strong track record of building tools, workflows, and analytical pipelines around proteomics technologies, and will be excited to work closely with Automation, Machine Learning, and Data Science teams to develop new experimental and computational capabilities. This role is well suited for someone who can execute at a high technical level while also rethinking proteomics systems end-to-end: designing fit-for-purpose workflows, rapidly prototyping new capabilities, and translating emerging ideas into differentiated experimental approaches that accelerate discovery. Here's how you will contribute: Design, execute, and optimize end-to-end proteomics workflows spanning sample preparation, LC-MS/MS data acquisition, data processing, and multi-experiment analysis in support of discovery and screening efforts. Build, prototype, and refine proteomics-centered tools, workflows, and analysis pipelines that enable rapid generation of high-quality data and scalable downstream interpretation. Construct tools and workflows for customizing DDA, DIA, and PRM acquisition methods, with clear understanding of the tradeoffs between these approaches and their fit-for-purpose applications. Partner closely with machine learning, data science, automation, and software teams to define requirements for proteomics data structure, quality control, metadata capture, and analytical capabilities. Lead the development of proteomics data processing, interpretation, and comparison approaches across experiments, campaigns, and datasets. Develop and adapt sample preparation methods for diverse proteomics applications, including workflows involving SP3, S-Trap, StageTips, immunoprecipitation, low-input samples, and related techniques. Operate, troubleshoot, and improve advanced proteomics workflows on state-of-the-art mass spectrometry platforms, with emphasis on Bruker timsTOF and Thermo Orbitrap instruments. Contribute to the design of platform capabilities to enable differentiated and proprietary approaches. Work across molecular biology, protein sciences, and drug discovery teams to identify opportunities where proteomics can unlock new experimental strategies, mechanistic insight, or screening modalities. Present findings clearly to multidisciplinary stakeholders and help shape experimental strategy, platform direction, and technical decision-making.

Requirements

PhD in Analytical Chemistry, Proteomics, Biochemistry, Molecular Biology, or a related field, with 4+ years relevant postdoctoral and/or industry experience; or MS with 8+ years industry experience.
Significant hands-on experience establishing and advancing mass spectrometry-based proteomics platforms, preferably in a biotech, pharmaceutical, or applied research environment.
Deep expertise across the full proteomics workflow, including sample preparation, chromatographic separation, mass spectrometry data acquisition, data processing, and downstream comparative analysis across experiments or datasets.
Strong experience designing, building, and improving proteomics workflows, analytical methods, or data pipelines rather than solely executing established assays.
Deep expertise with modern proteomics instrumentation, especially Bruker timsTOF and Thermo Orbitrap platforms.
Strong understanding of proteomics acquisition methods, including DDA, DIA, and PRM, and the ability to select, adapt, and optimize these approaches based on experimental goals.
Extensive experience with liquid chromatography for proteomics, including nano-ESI workflows, direct inject, and trap-and-elute.
Strong proficiency in proteomics data analysis tools, including PEAKS, Spectronaut, Skyline, Protein Metrics, and/or custom or open-source analysis workflows.
Extensive knowledge of proteomics sample preparation techniques, including SP3, S-Trap, StageTips, immunoprecipitation, single-cell proteomics, and low-input proteomics, with demonstrated experience developing and applying workflows across diverse sample types including purified proteins, cell lysates, tissues, and in vivo samples.
Demonstrated programming and computational problem-solving ability for scientific data analysis, workflow development, pipeline construction, and/or automation.
Strong experimental design, troubleshooting, and systems-thinking skills, with the ability to understand and improve proteomics workflows end to end.
Excellent communication and collaboration skills, with the ability to work effectively across proteomics, molecular biology, automation, machine learning, and data science teams.

Nice To Haves

Experience building or scaling proteomics data processing and analysis pipelines.
Experience working closely with machine learning, data science, or software teams to structure experimental data for downstream modeling and cross-study analysis.
Experience rapidly prototyping new proteomics capabilities in support of discovery goals.
Experience developing or improving proteomics sample preparation workflows for challenging, low-input, or high-throughput applications.
Experience with protein purification approaches using magnetic beads, resins, and high-throughput automation.
Working knowledge of protein production, DNA assembly, and protein purification strategies.
Experience applying proteomics in the context of biopharmaceutical or therapeutic protein discovery.
Experience using Benchling to support experimental design, sample and sequence tracking, and cross-functional collaboration in molecular biology and protein science workflows.
Familiarity with highly collaborative, matrixed research environments where analytical science, data science, and discovery biology are tightly integrated.

Responsibilities

Design, execute, and optimize end-to-end proteomics workflows spanning sample preparation, LC-MS/MS data acquisition, data processing, and multi-experiment analysis in support of discovery and screening efforts.
Build, prototype, and refine proteomics-centered tools, workflows, and analysis pipelines that enable rapid generation of high-quality data and scalable downstream interpretation.
Construct tools and workflows for customizing DDA, DIA, and PRM acquisition methods, with clear understanding of the tradeoffs between these approaches and their fit-for-purpose applications.
Partner closely with machine learning, data science, automation, and software teams to define requirements for proteomics data structure, quality control, metadata capture, and analytical capabilities.
Lead the development of proteomics data processing, interpretation, and comparison approaches across experiments, campaigns, and datasets.
Develop and adapt sample preparation methods for diverse proteomics applications, including workflows involving SP3, S-Trap, StageTips, immunoprecipitation, low-input samples, and related techniques.
Operate, troubleshoot, and improve advanced proteomics workflows on state-of-the-art mass spectrometry platforms, with emphasis on Bruker timsTOF and Thermo Orbitrap instruments.
Contribute to the design of platform capabilities to enable differentiated and proprietary approaches.
Work across molecular biology, protein sciences, and drug discovery teams to identify opportunities where proteomics can unlock new experimental strategies, mechanistic insight, or screening modalities.
Present findings clearly to multidisciplinary stakeholders and help shape experimental strategy, platform direction, and technical decision-making.