Director of Bioinformatics & Computational Antibody Engineering

Director of Bioinformatics & Computational Antibody Engineering

Valerio Therapeutics is a biotechnology company pioneering a new generation of precision-guided therapeutics for complex and underserved diseases across I&I (Immunology & Inflammation) and oncology.

At the heart of the company is the proprietary V-Body platform — built on two fully synthetic single-domain antibody (sdAb) libraries derived from a human VH and a fully humanized VHH. At roughly 15 kDa, these ultra-small antibody formats overcome the limitations of conventional biologics, enabling deep tissue penetration, highly precise target engagement, and superior therapeutic delivery to reach previously inaccessible targets. Coupled with a fully integrated chemistry platform for next-generation linker and payload conjugation, Valerio designs differentiated drug candidates with the potential to transform the treatment of high unmet-need diseases.

Position Summary

We are seeking a Director of Bioinformatics & Computational Antibody Engineering to build and lead the computational and AI engine that powers our V-Body discovery platform. Reporting directly to the Chief Technology Officer, you will embed structure-aware artificial intelligence across the full discovery cycle — from omics-driven target identification through de novo single-domain antibody design to lead optimization and developability — with each stage engineered directly into Valerio’s experimental discovery workflow.

This is a high-impact leadership role for someone who can both set strategy and stay close to the science. You will own the vision for how AI-driven antibody design and computational engineering accelerate Valerio’s R&D, build the team and infrastructure to deliver it, and partner closely with antibody engineering, chemistry, and translational teams to turn computational predictions into validated therapeutic candidates. You will join and grow Valerio’s existing computational team — currently one bioinformatician performing structure modelling and docking of phage-display-derived nanobodies, Schrödinger-driven affinity maturation, and de novo design — building on the company’s existing on-site high-performance computing infrastructure (two dedicated compute workstations).

• Work location: Villejuif (The Hive by Kadans, Campus Grand Parc)

Key Responsibilities

• Lead de novo antibody design, with a focus on single-domain antibodies (sdAbs / VHH / VH), using generative conformational structure approaches (diffusion-based structure generation, protein/antibody language models, inverse folding) to create novel binders against priority targets.
• Drive ML-driven antibody discovery and optimization across Valerio’s synthetic sdAb libraries — affinity maturation, epitope-focused design, multi-parameter optimization of developability properties (stability, solubility, aggregation, immunogenicity, expressibility), and in silico humanization.
• Apply AI to conjugate design and developability — informing linker/payload selection, site-specific conjugation, and candidate manufacturability in partnership with the chemistry platform.
• Integrate structure prediction and modeling tools (AlphaFold/ESMFold-class models, antibody-specific structure prediction, docking) into iterative design–build–test–learn cycles.
• Build in silico screening and triage workflows to prioritize candidates before wet-lab validation, compressing cycle time and reducing experimental cost.
• Define and execute the strategy for embedding AI/ML across the discovery platform, from data capture through candidate selection.
• Build scalable, reproducible computational infrastructure (cloud, pipelines, MLOps) — extending Valerio’s existing on-site high-performance computing resources (two dedicated compute workstations) for training, deploying, and monitoring models within production R&D workflows.
• Establish data architecture and governance — FAIR principles and structured capture of experimental results — so models continuously learn from internal data.
• Evaluate build-vs-buy decisions across external tools, platforms, and partnerships versus in-house development.

Target Identification & Multi-Omics

• Lead computational target identification and validation by interrogating public omics resources (e.g., TCGA, GTEx, Human Protein Atlas, DepMap, single-cell atlases) alongside proprietary, in-house–generated omics datasets.
• Build and apply predictive models and multi-omics integration pipelines (genomics, transcriptomics, proteomics, single-cell) to nominate and prioritize novel targets across oncology and autoimmune indications.
• Partner with biology and translational teams to connect target hypotheses to disease mechanism, expression specificity, accessibility for sdAb formats, and therapeutic rationale.
• Recruit, build, and mentor a multidisciplinary team of Computational Antibody Engineering at Valerio beginning with the direct management of Valerio’s existing bioinformatician.
• Set the technical vision and roadmap for AI in drug discovery, aligned with corporate and pipeline goals, in close partnership with the CTO.
• Collaborate cross-functionally with antibody engineering, chemistry, protein sciences, and translational teams.
• Communicate complex computational concepts to scientific and executive stakeholders; contribute to IP, publications, and partnership/BD discussions.
• PhD in computational biology, bioinformatics, machine learning, biophysics, computer science, or a related field — or equivalent experience.
• 8+ years of relevant experience in drug discovery with proven experience applying AI/ML tools.
• Deep expertise in machine learning applied to biological problems, with hands-on expertise in protein/antibody design or protein structure‑function modeling.
• Hands‑on experience with omics data analysis and multi‑omics integration.
• Strong programming skills (Python; ML frameworks such as PyTorch or TensorFlow) and experience with cloud computing and scalable data pipelines.
• A proven ability to translate computational work into real R&D impact and program decisions
• Direct experience in antibody or biologics discovery — single-domain antibodies / nanobodies, de novo design, affinity maturation, or developability prediction.
• Familiarity with generative protein models (diffusion models, protein language models, inverse folding) and modern structure-prediction tooling.
• Familiarity with molecular modelling and docking platforms such as the Schrödinger Suite.
• Exposure to antibody–drug conjugates or targeted-delivery modalities (linker/payload, bioconjugation).
• Experience standing up MLOps / production ML in an R&D environment.
• Track record of cross-functional leadership in a biotech, pharma, or platform company.
• Publications, patents, or recognized contributions in computational drug discovery.
• Familiarity with public multi-omics resources (e.g., TCGA, GTEx, Human Protein Atlas, DepMap) to inform target rationale.
• Capacity to independently design experiments and propose scientific solutions.
• Strong communication and presentation skills.
• Collaborative mindset and ability to thrive in multidisciplinary teams.
• Adaptability and resilience in a fast-paced biotech environment.
• Strong sense of ownership and accountability.

Our Values

• Scientific curiosity: Ability to explore complex topics with a proactive, solution-oriented mindset and strong problem-solving skills.
• Rigor and analytical mindset: Methodical approach, critical thinking when interpreting experimental data, with strong attention to detail and reproducibility.
• Team spirit : Close collaboration with R&D teams, respect for complementary expertise, and clear, constructive communication.
• Scientific integrity: Adherence to protocols, transparency in results, and a strong ethical commitment throughout the project.
• Adaptability : Agility in a fast-evolving environment, ability to handle unexpected challenges, and continuous learning from both successes and failures.
• Passionate about translating science into impact for patients.
• Salary: €70K -80K depending on profile.
• Annual individual performance bonus.
• 100% coverage of Vélib bike subscription.
• 60% coverage of family health insurance plan.
• Exciting, high-impact scientific projects.
• Supportive and dynamic work environment.
• Small, agile team with real responsibilities from day one.
• Modern offices, state-of-the-art equipment… and unlimited coffee