Senior Research Associate, Protein Science Developability
Lila SciencesCambridge, MA USAPosted 26 March 2026
Job Description
Your Impact at Lila
Lila Sciences is seeking an experienced Senior Research Associate specializing in Protein Purification, Analytics and Developability to support the build-out and day-to-day operation of a high-throughput protein characterization platform. In this role, you will contribute directly to the development of Lila's Scientific Superintelligence platform by executing and optimizing biophysical characterization workflows, collaborating effectively with cross-functional teams, and ensuring high-quality, reproducible data generation.
What You'll Be Building
Execute and optimize high-throughput workflows for protein characterization across a wide range of modalities, including antibodies, bi-specifics, and enzymes.
Operate and maintain automated chromatography systems, including Size Exclusion Chromatography (SEC), Hydrophobic Interaction Chromatography (HIC), and affinity chromatography using HPLC platforms.
Perform biophysical characterization using techniques such as Dynamic Light Scattering (DLS), melting temperature analysis (DSF), binding assays (BLI/SPR), capillary electrophoresis (CE-SDS, cIEF) and more, to assess protein critical quality attributes
Support workflows to characterize protein post-translational modifications (PTMs), ensuring comprehensive sample preparation and accurate data capture.
Collaborate closely with scientists, engineers, and automation experts to identify and implement improvements in efficiency, robustness, and scalability of developability workflows.
Innovate and adapt protocols to accommodate new protein formats, targets, and analytical strategies.
Ensure data quality, documentation, and reproducibility in all experiments; actively contribute to knowledge sharing and continuous improvement initiatives.
Collaborate with computational scientists to support data analysis pipelines, data processing, and the development of predictive models for protein characteristics.
What You’ll Need to Succeed
Bachelor's or Master's degree in Biochemistry, Biophysics, Molecular Biology, Bioengineering, or a related discipline with 2-5+ years of hands-on industry or academic experience in protein production and characterization.
Proven hands-on experience with HPLC techniques such as SEC, HIC, and affinity purification methods.
Familiarity with biophysical characterization techniques including DLS, DSF, BLI/SPR, CE-SDS, cIEF or similar analytical methods.
Experience with liquid handling automation and high-throughput formats.
Familiarity with laboratory data analysis, digital recordkeeping (e.g., Benchling, electronic lab notebooks), and collaborative scientific workflows.
Outstanding troubleshooting skills, adaptability, and ability to drive innovation in experimental design.
Strong communication skills and an eagerness to collaborate within a fast-paced, cross-functional team.
Bonus Points For
Familiarity with laboratory automation and high-throughput screening technologies.
Data analysis using relevant software (Python, R, or similar).
Experience with protein post-translational modification analysis workflows.
About Lila
Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at
www.lila.ai
If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, we encourage you to apply.
We’re All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, ma ... (truncated, view full listing at source)
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