Recruit Detail
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Company Name
MOLCURE Co., Ltd.
Job Type
Bioinformatics Scientist — Antibody Discovery & NGS
Work Detail
[The Role] We are looking for a Bioinformatics Scientist who will work across the full spectrum of antibody discovery—from mining screening data to computationally designing new candidate sequences. Working at the forefront of our antibody discovery engine, you will own the end-to-end NGS analysis pipeline, partner directly with wet-lab scientists to close the loop between computation and experiment, and collaborate with ML engineers to apply generative AI approaches for sequence design and optimization. This is not a downstream analysis role. You will shape which candidates enter our optimization pipeline—whether discovered from screening data or computationally designed—and your biological interpretation will directly influence lead selection decisions. As an early member of the computational team, you will have the autonomy to define how our discovery informatics evolves. [Position Overview] We are seeking a Bioinformatics Scientist who will work across the entire spectrum of antibody discovery, from mining screening data to designing novel candidate sequences using generative AI. This role is at the forefront of antibody discovery engines, offering end-to-end ownership of the NGS analysis pipeline. You will work directly with wet lab scientists to close the computation-experiment loop, and collaborate with ML engineers to drive generative AI approaches to sequence design and optimization. This is not downstream analysis work. You will determine which molecules enter the optimization pipeline, whether candidates discovered through screening or computationally designed, and your biological interpretations will directly impact lead selection decisions. As an early member of the computational team, this position offers autonomy, allowing you to define the direction of discovery informatics evolution. What You Will Do • NGS Pipeline Development & Execution Build, execute, and continuously refine pipelines for processing high-throughput sequencing data from antibody display and screening campaigns. Ensure reproducibility, scalability, and rapid turnaround to keep pace with experimental cycles. • Candidate Discovery & Prioritization Analyze clonal enrichment, sequence diversity, and predicted developability to identify and rank antibody leads. Provide data-driven rationale for which candidates advance to downstream characterization. • AI-Driven Sequence Design Collaborate with ML engineers to leverage generative AI and protein language models for de novo antibody sequence design and optimization. Evaluate computationally designed candidates using the same biological and developability criteria applied to screening-derived hits, bridging the gap between generative models and experimental validation. Wet-Lab Partnership Serve as the primary computational partner to bench scientists. Interpret screening results collaboratively, provide rapid feedback on experimental outcomes, and co-design iterative screening strategies based on data insights. • Pipeline & Codebase Evolution Maintain and improve our Python-based analysis codebase. Architect flexible, modular systems that can incorporate new algorithms, public datasets, and evolving experimental formats as the field advances. • Scientific Intelligence Monitor the rapidly evolving landscape of AI-driven antibody discovery and NGS methodology. Evaluate emerging tools and approaches, and bring validated improvements into our production workflows. [Responsibilities] • NGS Pipeline Development and Execution Build, execute, and continuously improve pipelines that process high-throughput sequencing data obtained from antibody display and screening campaigns. Ensure reproducibility, scalability, and rapid turnaround to keep pace with the experimental cycle. • Candidate Antibody Discovery and Prioritization Analyze clonal enrichment, sequence diversity, and predicted development potential to identify and rank antibody leads. Provide data-driven rationale for which candidates should proceed to downstream characterization. • AI-Powered Sequence Design Collaborate with ML engineers to drive de novo antibody sequence design and optimization using generative AI and protein language models. Computationally designed candidates are evaluated using the same biological and developmental feasibility criteria as screening-derived hits, bridging the gap between generative models and experimental validation. • Wet Lab Collaboration Serving as a primary computational partner for bench scientists. Collaboratively interpreting screening results, providing rapid feedback on experimental results, and co-designing data-insight-based, iterative screening strategies. • Pipeline Codebase Evolution Maintaining and improving the Python-based analysis codebase. Designing a flexible and modular system that can accommodate new algorithms, public datasets, and evolving experimental formats. • Scientific Intelligence Monitoring the rapidly evolving trends in AI-driven antibody discovery and NGS methodologies. Evaluating emerging tools and approaches and introducing validated improvements into production workflows.
Ideal Profile
What We Are Looking For We need someone who can work across the full arc of discovery informatics—from raw FASTQ files to biological insight. While AI and automation dramatically accelerate throughput, we require a scientist who understands the biology deep enough to catch what algorithms miss and to know when a computational result demands experimental validation. [Foundational Expertise (Required)] • Master's degree or PhD in Bioinformatics, Computational Biology, Immunology, Molecular Biology, or a related quantitative field. • 3+ years of hands-on experience in NGS data analysis, with a focus on antibody or immune repertoire sequencing (PhD research counts toward this requirement). • Strong understanding of antibody biology: V(D)J recombination, clonal selection, affinity maturation, and the sequence-function relationship in antibody engineering. You should be able to assess a candidate's therapeutic potential from sequence data, not just statistical enrichment. • Proficiency in Python for bioinformatics workflows. Comfortable building and maintaining reproducible pipelines (e.g., Snakemake, Nextflow), not just running one-off analyses. • Working familiarity with standard NGS toolchains (e.g., FASTQC, Trimmomatic/Cutadapt, alignment and clustering tools) and biological databases (IMGT, PDB, UniProt). [AI & Computational Tool Proficiency (Required)] • Experience applying machine learning or statistical methods to sequence analysis tasks—such as enrichment scoring, clustering, or developability prediction—and understanding when these methods add value versus when manual curation is more appropriate. • Experience using LLM-based AI assistants (e.g., Claude, ChatGPT, GitHub Copilot) or other AI productivity tools to accelerate research workflows—such as code development, literature analysis, data interpretation, or pipeline debugging. We value scientists who actively leverage AI to multiply their output while maintaining scientific rigor. • Familiarity with protein language models (e.g., ESM-2, AntiBERTy) or antibody-specific computational tools (e.g., ANARCI, AbNum, IgBLAST) are a strong plus. [Desired Candidate Profile] We are looking for someone who can traverse the entire process of discovery informatics, from raw FASTQ files to biological insights. While AI and automation dramatically accelerate throughput, we need scientists with a deep understanding of biology who can capture what algorithms miss and determine when computational results require experimental validation. [Required Basic Expertise] • Master's or PhD in Bioinformatics, Computational Biology, Immunology, Molecular Biology, or a related quantitative field • 3+ years of practical experience in NGS data analysis, particularly focused on antibody or immunorepertoire sequencing (doctoral research is included in this requirement) • Solid understanding of antibody biology: V(D)J recombination, clonal selection, affinity maturation, sequence-function relationships in antibody engineering. Ability to evaluate the therapeutic potential of candidates from sequence data, as well as statistical enrichment. • Proficiency in implementing Python for bioinformatics workflows. Ability to build and maintain reproducible pipelines (Snakemake, Nextflow, etc.). Ability to create systems that can be continuously operated, not just one-off analyses. • Practical knowledge of standard NGS toolchains (FASTQC, Trimmomatic/Cutadapt, alignment and clustering tools, etc.) and biological databases (IMGT, PDB, UniProt) [AI and Computational Tool Utilization Skills (Required)] • Experience applying machine learning and statistical methods to sequence analysis tasks (enrichment scoring, clustering, development feasibility prediction, etc.). Ability to determine when these methods are valuable and when manual curation is more appropriate. • Experience using LLM-based AI assistants (Claude, ChatGPT, GitHub Copilot, etc.) and other AI productivity tools to accelerate research workflows. We are seeking scientists who can leverage AI in code development, literature analysis, data interpretation, and pipeline debugging to dramatically increase productivity while maintaining scientific rigor. • Knowledge of protein language models (ESM-2, AntiBERTy, etc.) and antibody-specific computational tools (ANARCI, AbNum, IgBLAST, etc.) is a strong plus. Qualities We Value - Biological intuition: You don't just run pipelines—you interpret results through the lens of antibody biology and can explain why a statistically enriched clone may or may not be a good therapeutic candidate. - Engineering discipline: You write clean, documented, and testable code. You think about reproducibility and maintainability, not just getting an answer once. - Intellectual curiosity: You proactively track new methods in computational immunology and AI for drug discovery, and you bring validated ideas back to the team. - Startup adaptability: You thrive in an environment where priorities shift, experimental designs evolve mid-campaign, and you're expected to figure things out. Out rather than wait for instructions. Qualities We Value - Biological Intuition: The ability to interpret results through the lens of antibody biology, not just run pipelines. The ability to explain why statistically enriched clones are not always good therapeutic candidates. - Engineering Discipline: The ability to write clean, documented, and testable code. The ability to design with reproducibility and maintainability in mind, not just to get the answer once. - Intellectual Curiosity: The ability to proactively track new methods in computational immunology and AI-driven drug discovery, and bring validated ideas back to the team. - Startup Adaptability: The ability to thrive in an environment where priorities change, experimental designs evolve mid-campaign, and where you are required to think and act independently rather than waiting for instructions. Required Languages & Proficiency Level • Business level / Fluent English Preferred Languages & Proficiency Level • Conversational Japanese
What we especially look for in a PhD or postdoctoral fellow
• Master's degree or PhD in Bioinformatics, Computational Biology, Immunology, Molecular Biology, or a related quantitative field. • 3+ years of hands-on experience in NGS data analysis, with a focus on antibody or immune repertoire sequencing (PhD research counts toward this requirement). • Master's or PhD in Bioinformatics, Computational Biology, Immunology, Molecular Biology, or a related quantitative field. • 3+ years of practical experience in NGS data analysis, with a particular focus on antibody or immune repertoire sequencing (PhD research counts toward this requirement).
Work Location
Full remote work available (Asia–Europe time zones only) Meguro Ward, Tokyo
Phd. Stating Salary
• Commensurate with experience.