Jobs at Eikon Therapeutics

The Director, High-Throughput Biology (HTB) is responsible for the strategy, performance, and evolution of Eikon’s high-throughput screening and discovery platforms. This role leads end-to-end high-throughput screening execution across automated and imaging-based platform technologies, ensuring delivery of scalable, reproducible, decision-enabling data that supports portfolio priorities. Operating at the intersection of biology, automation engineering, and computational science, this leader establishes standards for assay robustness, data quality, platform reliability, and technical execution, while building a high-performing organization capable of advancing discovery programs efficiently and at scale.

About You

You are a strategic and technically grounded leader who brings deep expertise in high-throughput screening and platform biology, along with the ability to lead across biology, automation, and computational disciplines. You are comfortable operating at both the strategic and technical level, balancing organizational leadership with hands-on scientific engagement where needed. You bring strong judgment, cross-functional leadership, and a commitment to building scalable, resilient systems that support high-quality discovery execution. You are also experienced in developing teams, strengthening technical depth, and creating shared ownership across critical workflows and capabilities.

• Set the vision and operating model for HTB, defining strategy, success metrics, and standards that ensure sustainable, reliable, high-quality screening data delivery across the portfolio.
• Partner cross-functionally at the assay-to-screening interface to align assay readiness, screening feasibility, platform capabilities, and project priorities.
• Lead high-throughput screening strategy and execution, partnering with HTB scientists and program leadership to drive prioritization, ensure feasibility, and enable data-driven decisions.
• Optimize team resourcing and representation to support screening design, execution, analysis, and program engagement across scientists and SRAs.
• Lead an integrated platform organization that aligns biology, automation, and data analytics into a cohesive screening engine, including workflows, robotics integration, quality control, reporting, and data practices.
• Build, develop, and retain a high-performing team through clear accountability, coaching, and development of technical and cross-functional capability.
• Provide hands-on technical leadership by shaping assay strategy, screening design, analytical methods, and data or automation tooling where needed.
• Establish scalable practices through documentation, playbooks, and shared ownership of critical workflows and code to strengthen continuity and reduce single-point dependency.

• PhD in Biological Sciences, Computational Sciences, or a related discipline with 10+ years of experience in high-throughput or high-content screening environments.
• Demonstrated expertise with automated screening platforms, including 384- and/or 1536-well formats.
• Experience screening compound libraries larger than 100k.
• Experience advancing drug discovery programs from hit identification through lead optimization.
• Experience with microscopy-based or imaging-enabled screening platforms.
• Experience with genetic screens or systems biology approaches.
• Strong expertise in statistical analysis and large-scale dataset quality control, with proficiency in Python.
• Experience building or contributing to scalable data processing pipelines.
• Proficiency with compound databases, LIMS, and enterprise data systems.
• Proven cross-functional leadership across biology, chemistry, engineering, and data teams.
• Experience with single-molecule imaging technology is preferred.
• Experience with novel technologies or assay development for drug discovery is preferred.
• Strong computational science capability and familiarity with machine learning or artificial intelligence applications in phenotypic or multiparametric screening are preferred.
• Demonstrated ability to build hybrid biology and computational teams is preferred.