Senior/Principal Scientist, Small Molecule Therapeutics

Lila Sciences
Cambridge
Workplace: OnsiteFull timeUSD 148,000 - 208,000Function: Research & Scientific (R&D)Experience: 6-8 yearsEducation: phdSkills: ["Scientific communication","Stakeholder alignment","Data interpretation","Cross-functional collaboration"]

Lead early hit identification for small molecule therapeutics using DNA-encoded libraries (DEL) and complementary screening technologies. Partner with Chemistry, Computational, and Automation teams to build and scale a small-molecule screening platform, design DEL screening campaigns, and drive hit progression through optimized biochemical and biophysical assay cascades. Establish protein supply and protein constructs for high-throughput screening, collaborate on assay development, and translate data into hit-to-lead workflows.

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Lila Sciences
Lila Sciences
2 months ago

Senior/Principal Scientist, Small Molecule Therapeutics

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Job Summary

Lead early hit identification for small molecule therapeutics using DNA-encoded libraries (DEL) and complementary screening technologies. Partner with Chemistry, Computational, and Automation teams to build and scale a small-molecule screening platform, design DEL screening campaigns, and drive hit progression through optimized biochemical and biophysical assay cascades. Establish protein supply and protein constructs for high-throughput screening, collaborate on assay development, and translate data into hit-to-lead workflows.
Location: Cambridge
Workplace: Onsite
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Lead early hit identification by building and continuously improving DNA-encoded library (DEL) screening capabilities integrated with an AI-enabled chemistry design engine and automated/robotic synthesis.
  • •Design and execute DEL screening campaigns, including selection strategy, experimental execution, data interpretation, and translation into hit validation and hit-to-lead workflows.
  • •Develop, optimize, and run biochemical and biophysical assay cascades to validate and characterize hits from the screening platform.
  • •Evaluate and operationalize complementary screening modalities (e.g., fragment, affinity-based, functional) to expand hit-finding coverage and diversify chemotypes.
  • •Secure high-quality protein supply by establishing collaborations and defining/producing protein constructs to enable reliable screening and downstream hit validation.

Pay and Benefits

Salary: USD 148,000 - 208,000
Perks:Health InsuranceDentalVisionLife InsuranceDisability InsurancePaid Parental

Key Requirements

  • •PhD in Biology, Chemical Biology, Biochemistry, or related field with 6–8 years of relevant drug discovery experience, or MS/BS with 10+ years in small molecule discovery, screening, and hit finding.
  • •Demonstrated success designing, executing, and delivering DNA-encoded library (DEL) screening campaigns that produce validated hits for downstream chemistry.
  • •Strong working knowledge of complementary screening approaches (e.g., fragment-based, affinity-based, functional/phenotypic) to diversify hit sources and chemotypes.
  • •Experience developing and optimizing biochemical and biophysical assays to triage, validate, and characterize screening hits, including assay quality metrics and troubleshooting.
  • •Hands-on experience with affinity/biophysics methods for hit confirmation and ranking (e.g., SPR, BLI, DSF, ITC, MST) and the ability to interpret data critically.
Experience:6-8 years
Education:PhD / Doctorate
Skills:Scientific communicationStakeholder alignmentData interpretationCross-functional collaboration
Tech Stack:DNA-encoded libraries (DEL)AI-enabled chemistry design engineMachine learningAutomationRoboticsLiquid handlingSPRBLIDSFITCMSTAffinity-based methodsBiochemical assaysBiophysical assaysFunctional/phenotypic screeningProtein constructsAI-enabled drug discovery

Company Brief

Lila Sciences
Develops AI-driven platforms to accelerate drug discovery and biological research by integrating machine learning with chemical and biological data to predict molecular properties, streamline candidate selection, and enable faster therapeutic development.
Industry: Biotech
Company Size: Small (11 to 50 employees)
Growth: Early Stage Startup
Funding: Seed
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