The Opportunity:

We are seeking a highly motivated Research Associate II to support day-to-day laboratory operations within the Sample Management function. This role is ideal for a hands-on laboratory professional who thrives in a fast-paced research environment and takes pride in operational excellence, data integrity, and high-quality execution.

The successful candidate will play a critical role in supporting compound and biological sample workflows, ensuring accurate tracking, storage, and distribution of materials that enable discovery and development programs.

Key responsibilities

• Execute core sample management activities, including sample receipt, accessioning, storage, tracking, retrieval, distribution, and disposal.

• Operate and maintain automated storage systems, LIMS, and supporting laboratory infrastructure.

• Perform routine quality control checks to ensure sample integrity, data accuracy, and system reliability.

• Accurately document all activities in LIMS and associated databases in compliance with SOPs and data integrity standards.

• Troubleshoot routine equipment or process issues and escalate complex problems as needed.

• Support implementation, maintenance, and continuous improvement of SOPs and best practices.

• Partner closely with cross-functional teams to ensure timely and accurate delivery of compounds and samples.

• Assist with onboarding of new technologies, automation platforms, and laboratory systems.

• Contribute to process improvement initiatives to enhance efficiency, scalability, and reliability of sample management workflows.

• Maintain laboratory organization, compliance, and operational readiness.

Required Skills, Experience and Education:

• BS or MS in chemistry, biology, engineering, or related scientific discipline.

• Typically 3+ years of relevant laboratory experience with increasing technical responsibility.

• Proven ability to independently execute and prioritize laboratory workflows.

• Experience troubleshooting laboratory equipment or operational processes.

• Demonstrated contributions to process optimization or workflow improvements.

• Strong commitment to data integrity and operational discipline.

• Clear written and verbal communication skills.

• Proficiency with standard laboratory software and data systems.

Preferred Skills:

• Experience supporting small-molecule drug discovery programs.

• Familiarity with automated compound/sample storage platforms.

• Exposure to laboratory automation systems.

• Experience with Cenevo’s Sample Management Software Mosaic.

• Experience contributing to continuous improvement initiatives.  

  #LI-Hybrid  #LI-LN1

The Opportunity:

Revolution Medicines is seeking a scientifically talented and motivated individual to join our Integrated Discovery Research team as Associate Director, Analytical Chemistry. This role will drive analytical chemistry efforts within the Medicinal Chemistry function and collaborate cross-functionally to support our drug discovery programs from early hit identification through lead optimization and candidate selection.  Reporting to the Director of Medicinal Chemistry, this position offers a unique opportunity to shape analytical science at the frontiers of small molecule beyond Rule-of-Five (bRo5) drug discovery.

• Lead and manage analytical chemistry strategy and daily operations to support small molecule drug discovery programs across multiple stages.

• Develop and implement robust analytical methods (e.g., LC/MS, HPLC, SFC) for compound characterization, purity assessment (including chiral purity), and stability or reactivity studies (e.g. GSH assay).

• Manage and execute compound purification (including chiral separations), structural elucidation, and physicochemical property assessment in the beyond Rule-of-Five chemical space.

• Collaborate closely with medicinal chemists, process chemists, DMPK scientists, and manage external analytical chemistry service providers to ensure timely and high-quality analytical data.

• Lead and mentor a team of analytical chemists, fostering scientific excellence and professional development.

• Collaborate closely with LabOS team, Data Sciences and Data Analytics functions to develop and implement automated high-throughput analytical methods that enhance efficiency and data quality.

• Ensure compliance with safety standards in laboratory operations.

• Contribute to invention disclosures, patent filings, and external scientific publications as appropriate.

Required Skills, Experience and Education:

• Ph.D. in Analytical or Organic Chemistry (or related discipline) with 5+ years of industry experience, or M.S. with 10+ years of relevant experience.

• Demonstrated success in analytical method development, troubleshooting and problem-solving for small molecule drug discovery.

• Strong hands-on knowledge of chromatographic and spectroscopic techniques, including LC/MS, HPLC, and SFC.

• Strong leadership and people management capabilities with a collaborative, mentoring mindset.

• Strong problem-solving skills with sound technically driven decision-making ability.

• Excellent communication skills and the ability to drive cross-disciplinary partnerships.

• A track record of innovation and execution in fast-paced, data-driven research environments.

Preferred Skills:

• Experience supporting beyond Rule-of-Five (bRo5) medicinal chemistry, including macrocycles, covalent inhibitors, or other alternative modalities.

• Ability to integrate analytical insights into medicinal chemistry decision-making.

• Working knowledge of lab automation and high-throughput analytical techniques.

• Experience with advanced NMR spectroscopy. 

  #LI-Hybrid  #LI-LN1

The Opportunity:

We are seeking a motivated Senior Scientist I to join our dynamic Computational Chemistry team.This position offers an exciting opportunity to drive drug discovery through cutting-edge computational methods, working at the intersection of structural biology, medicinal chemistry, biology, and data science to accelerate the development of transformative therapeutics for patients.

As a key contributor in our Computational Chemistry team, you will:

• Partner with multidisciplinary, collaborative teams in Discovery Sciences to design, optimize, and accelerate the discovery of novel drug candidates using structure-based computational methods.

• Analyze structure-activity relationships (SAR) and make data-driven decisions by systematically following existing SAR trends to guide compound prioritization and design strategies for Ro5 and bRo5 molecules.

• Partner closely with Medicinal Chemists to prioritize compound designs both independently and collaboratively, effectively communicating scientific rationale and recommendations to diverse interdisciplinary teams.

• Design and implement virtual screening workflows leveraging both structure-based and ligand based computational methods to discover novel hit compounds and accelerate lead optimization processes.

• Leverage advanced computational methodologies, including physics-based methods, generative models and machine learning approaches, to enhance understanding of SAR and drive Ro5 and bRo5 projects with timely decision making to ensure focus and delivery of key objectives.

• Provide innovative ideas and models to challenge and guide project roadmap while serving as lead computational chemistry representative on drug discovery pipeline projects

• Devise and execute strategies to augment our hit finding abilities for novel protein and protein protein targets, aid in designing diverse chemical libraries.

Required Skills, Experience and Education:

• Ph.D. in computational chemistry or related field.

• 5+ years of computational chemistry experience in the biotechnology/pharmaceutical industry alongside a strong track record of positive impact in programs demonstrated by publications, patents, and/or presentations.

• Expert-level knowledge of contemporary computational chemistry methods and their use in protein-ligand analysis and drug design such as docking, virtual screening, pharmacophore modeling, MD simulation, QM calculations etc.

• n-depth experience with computational chemistry, cheminformatics, data science, machine learning software.

• Scientific programming or scripting in Bash, Python, R, Java, C languages, or similar languages.

• Excellent oral and written communication skills. Experience in positively influence team decisions and effectively collaborate across multidisciplinary environments.

• Driven by a desire to be innovative and creative in a high energy, fast-paced environment.

Preferred Skills:

• Experience with diverse Ro5 and bRo5 therapeutic modalities such as macrocycles and small molecules.

• Working in HPC environments.

• Strong publication history demonstrating computational chemistry expertise and innovation.

• Familiarity with data visualization tools and analytics tools for effective communication of insights.

#LI-Hybrid #LI-LN1

The Opportunity:

As Executive Director, AI/ML Drug Discovery Analytics, you will be a senior leader shaping the future of data-driven drug discovery at Revolution Medicines. You will define the Research’s AI/ML strategy, oversee implementation of advanced analytics across discovery and build a world-class team to unlock the full potential of our rich internal datasets and make these insights accessible to other scientist. Your leadership will directly influence early discovery innovation, pipeline acceleration, and strategic portfolio decisions.

Responsibilities include:

Technical Leadership and Innovation: You will have a deep understanding of Machine Learning Operations and AI system design.  Design and scale a robust ML infrastructure, integrating MLOps best practices to ensure seamless development, deployment, and monitoring of AI/ML models.

Drive innovation in applying deep learning, generative models, and diffusion models to drug discovery, including target prediction, hit identification, and lead optimization. Leverage RevMed’s unique datasets to generate novel hypotheses and enable data-driven decision-making across the research continuum.

Strategic vision and Leadership: Define the long-term vision for AI/ML-enabled discovery and align it with organizational priorities.  Build, mentor, and inspire a cross-disciplinary team of data scientists, computational chemists, engineers, and biologists.  Establish key performance metrics and ensure delivery of impactful solutions that accelerate portfolio progression.

External and Cross-functional Collaboration: Forge partnerships with leading academic groups, technology innovators, and compute providers to access cutting-edge platforms and methodologies. Collaborate across research, chemistry, biology, and IT to identify high-impact opportunities for AI/ML integration and ensure smooth deployment of scalable solutions.  Act as a thought leader internally and externally, representing RevMed at scientific forums and shaping the broader field of AI-driven drug discovery.  Work closely with the Chief Digital Officer and team to build and drive the research vision for advanced analytics within the enterprise framework.  Lead the cross functional analytics team to harness data science and AI to accelerate early drug discovery.  

Required Skills, Experience and Education:

• Ph.D. or Masters degree in Computer Science, Machine Learning, Physics, Math or a relevant scientific discipline with over 10+ years’ experience, exposure to drug discovery is a plus.

• Expertise in a wide variety of AI/ML-based computational techniques and developing adaptable ML workflows to solve challenging problems.

• Deep understanding of AI/ML techniques such as deep learning, reinforment learning and generative models.

• Expertise in frameworks such as PyTorch/TensorFlow, training, and fine-tuning models on GPUs.

• Track record of deploying AI/ML solutions at scale.

• Proven track record of leadership and cross-functional collaboration across ML-Scientist, Software Engineering, and MLOps.

• Previous experience leading large AI/ML projects that require engaging with collaborators across the board and with varying degrees of expertise.

• Expertise in machine learning infrastructure and MLOps, including cloud and on-prem compute environments.

• Demonstrated ability to build, scale, and lead high-performing cross-functional teams.

• Strong communication and leadership skills with the ability to bridge diverse scientific and technical disciplines.

• Passion for scientific innovation and a relentless commitment to improving patient outcomes.

Preferred Skills:

• Proven track record of applying advanced AI/ML approaches (deep learning, generative modeling, structure-based ML) to drug discovery or related life sciences domains.

• Knowledge of oncology therapeutic area and drug discovery.

• Strong familiarity with biological and chemical datasets and compound libraries.

• Evidence of successful coaching, mentorship and development of both individuals and teams in order to build long-term organizational capability.

#LI-Hybrid  #LI-GL1

The Opportunity:

We are seeking a Scientific Writer with strong nonclinical regulatory experience to support the authoring and integration of nonclinical content for U.S. and global regulatory submissions. This role focuses on developing and integrating nonclinical content beyond INDs and NDAs, including briefing documents, Investigator Brochures, responses to health authority questions, and other submission-related materials across the U.S., Europe, and Asia.

As a strategic scientific integrator, this individual will translate complex RAS biology and translational pharmacology into clear, compelling regulatory narratives that reflect RevMed’s deep mechanistic understanding of RAS signaling and frontier targets. The role partners closely with nonclinical subject matter experts (SMEs) in pharmacology, toxicology, and DMPK, as well as Regulatory Affairs, Regulatory Operations, and Medical Writers, to ensure nonclinical strategy and content are scientifically rigorous, aligned, and submission-ready.

The Scientific Writer will leverage structured source documents, document management systems, and emerging AI-enabled tools to improve efficiency, consistency, and scalability of nonclinical regulatory writing. This role requires strong organizational skills and the ability to manage multiple programs and cross-functional inputs simultaneously in a fast-paced, scientifically rigorous environment.

Key Responsibilities

• Author, integrate, and edit nonclinical sections of regulatory documents across multiple submission types, including INDs, NDAs, briefing documents, amendments, and response to health authority requests.

• Safeguard and articulate the scientific rationale underlying RevMed’s RAS(ON) and companion inhibitor programs across regulatory communications.

• Develop clear, hypothesis-driven regulatory narratives that integrate pharmacology, toxicology, and DMPK data, and engage as a critical scientific thought partner to these functions to strengthen mechanistic coherence, regulatory positioning, and alignment with global regulatory expectations (FDA, EMA, and select Asian agencies).

• Collaborate closely with project Medical Writers to ensure nonclinical sections are delivered in alignment with overall timelines and coordinated with related document sections.

• Ensure scientific consistency across programs to preserve and extend RevMed’s reputation for rigor and mechanistic depth.

• Manage multiple regulatory writing efforts in parallel, balancing priorities across programs and functions in a fast-paced environment.

• Utilize Regulatory information management systems (e.g., Veeva) and AI-enabled writing tools to support authoring, review, and lifecycle management of regulatory documents.

• Contribute to templates, guidance, and best practices for global nonclinical regulatory documentation.

• Drive continuous improvement of nonclinical authoring standards, templates, and knowledge management systems.

• Capture and institutionalize lessons learned from prior submissions to improve quality, consistency, and efficiency across programs and regions.

Required Skills, Experience and Education:

• Advanced degree (PhD preferred; MS considered) in a relevant nonclinical scientific discipline.

• Demonstrated experience authoring or substantially contributing to nonclinical sections of regulatory submissions across multiple document types.

• Strong working knowledge of pharmacology, toxicology, and DMPK and their integration into regulatory narratives.

• Demonstrated ability to synthesize complex mechanistic datasets into coherent, hypothesis-driven regulatory narratives.

• Comfortable engaging deeply in data discussions with subject matter experts and operating in a culture of scientific debate and rigorous review.

• Familiarity with range of regulatory expectations including those beyond the U.S. such as European and/or Asian health authorities.

• Proven ability to manage multiple projects concurrently and work effectively across functions.

• Excellent written communication skills, attention to detail, and a collaborative, solutions-oriented mindset.

Preferred Skills:

• Industry experience as a Scientific or Medical Writer.

• Experience supporting global regulatory submissions (FDA, EMA, etc.).

• Proficiency with Veeva or similar Regulatory information management systems platforms.

• Experience leveraging AI-enabled tools for scientific or regulatory writing.

• Comfortable operating in a dynamic, evolving research and development environment. #LI-Hybrid  #LI-LN1

The Opportunity:

We are seeking a Senior Machine Learning Scientist to help accelerate drug discovery through advanced analytics and artificial intelligence. This role will develop predictive models and analytical methods that transform complex biological and chemical datasets into actionable insights that guide research decisions.

The Senior Machine Learning Scientist will work at the interface of data science, chemistry, and biology to support target discovery, compound optimization, and translational research. This position requires both strong machine learning expertise and the ability to collaborate effectively with experimental scientists to solve real-world scientific problems.

The successful candidate will contribute to building a data-driven discovery ecosystem where data, analytics, and experimentation continuously inform and accelerate one another.

Key responsibilities include:

Develop Predictive Models for Drug Discovery

• Design and implement machine learning models to predict compound activity, selectivity, and developability.

• Develop predictive frameworks for ADME/Tox, target engagement, and phenotypic screening outcomes.

• Apply advanced modeling approaches including deep learning, graph neural networks, and ensemble methods.

• Evaluate model performance and apply appropriate validation strategies.

• Work with data engineers and ML engineers to integrate models into discovery pipelines.

Analyze Complex Scientific Data

• Perform exploratory data analysis on chemical, biological, and phenotypic datasets.

• Integrate heterogeneous datasets including:

• Chemical structure and screening data.

• High-content imaging data.

• Structural biology and molecular simulation outputs.

• Identify patterns and relationships that inform scientific hypotheses.

Collaborate with Research Scientists

• Partner with medicinal chemists to support compound design and lead optimization.

• Work with biologists to interpret experimental results and identify new target opportunities.

• Translate scientific questions into computational modeling strategies.

Required Skills, Experience and Education:

• PhD in machine learning, computational biology, computational chemistry, computer science, statistics, or a related quantitative field.

• 4–8 years experience applying machine learning or advanced analytics to scientific datasets.

• Python and scientific computing libraries (NumPy, Pandas, SciPy).

• Machine learning frameworks (PyTorch, TensorFlow, scikit-learn).

• Model development, validation, and evaluation methods.

• Data visualization and exploratory analysis.

• Experience working with noisy and incomplete experimental datasets.

Preferred Skills:

• Cheminformatics or molecular modeling tools (RDKit, OpenEye, etc.).

• Multi-omics data analysis.

• Cloud computing environments.

• MLOps or scalable model deployment. 

  #LI-Hybrid  #LI-LN1

The Opportunity:

Revolution Medicines is seeking a motivated individual with a scientific background to play a critical role as a technical leader within the CRO management team in the Medicinal Chemistry function. This position will be responsible for route evaluation and enabling the CRO team to perform reaction condition optimization, and scale-up of key intermediates to support Revolution Medicines Medicinal Chemistry efforts. The qualified candidate will be responsible for enabling the rapid transition of the Medicinal chemistry routes to PDM process chemistry development, the effective communication and collaboration with CRO teams and PDM and leading efficient transitional efforts to enable the PDM team. This role is reporting to the Director of CRO management, Medicinal Chemistry.

Responsibilities:

• Work closely with discovery chemistry teams and monitor the progress of the projects and provide support on material needs of building blocks and intermediates and solutions of challenging chemistry.

• Explore the chemistry on route scouting and optimization of key building blocks, intermediates for development candidates, validate the synthetic and analytical protocol, and evaluate the safety of scale up.

• Propose chemistry for building blocks and intermediates, including route evaluation and process optimization utilizing advanced technologies like flow chemistry, photochemistry, biocatalysis etc. to solve complex synthesis challenges.

• Manage internal and external teams to scale up intermediates to support discovery chemistry efforts from gam-scale to kilogram-scale production.

• Improve the overall efficiency of targets synthesis both for discovery chemistry and CMC process chemistry, provide manufacturing friendly solutions on candidate synthesis.

• Manage technology transfer from discovery chemistry to CMC process chemistry and help on process familiarization at CDMOs (contract development and manufacturing organization).

• Work with project team(s) to identify development candidate requirements, manage and optimize aggressive project timelines.

• Write, review, and approve study protocols, manufacturing batch records, and development and campaign reports.

• Communicate effectively within cross functional project teams at Revolution Medicines.

• Stay current with organic chemistry literature and emerging technologies

Required Skills, Experience and Education:

• Advanced degree (PhD or MS) in organic chemistry or related discipline.

• Minimum of5+ years of relevant experience in Medicinal Chemistry or pharmaceutical process research and development.

• Strong foundation and delivery track record in synthetic organic chemistry.

• Strong Communication skills.

• Proficiency in modern characterization techniques (NMR, LC-MS, HPLC).

• Strong problem-solving skills with sound technically driven decision-making ability.

• Effective written and verbal communication skills and interpersonal skills.

• An innovative team-player with capacity to be effective in a dynamic and fast-paced company environment.

Preferred Skills:

• PhD in organic chemistry, chemical engineering or related discipline with 5+ years of relevant industry experience in small molecule drug discovery and/or development.

• Previous experience managing projects at CROs/CMOs.

• Knowledge and experience in non-good manufacturing practices (GMP) and GMP API process development, scale-up, and manufacturing is a plus.

• Track record of patents and/or peer-reviewed publications. 

  #LI-Hybrid  #LI-LN1

The Opportunity:

We are seeking an experienced Director of Screening Platform Technology to lead a multidisciplinary team responsible for the design, execution, and evolution of Revolution Medicines’ screening platforms. This role will oversee assay operations, new process implementation, and screening analytics and controls, and will partner closely with software engineers, automation engineers, and a cross-functional screening strategy taskforce. The successful candidate will bring strong people leadership, deep experience in high-throughput screening platforms, and direct technical expertise in screening analytics and data-driven experimental design.

• Lead and develop a team of approximately 7–10 scientists and technical staff across assay operations, new process implementation, and screening analytics and controls.

• Provide strategic and technical leadership for HTS platform development in close partnership with software engineering, automation engineering, and cross-functional stakeholders.

• Own the end-to-end screening platform, ensuring robust integration of robotics, instrumentation, software, and data pipelines.

• Drive adoption of advanced screening analytics, including quality control frameworks, longitudinal analysis across campaigns, and AI-enabled or data-driven experimental design.

• Serve as a technical thought partner to discovery teams, translating scientific questions into effective screening strategies and interpretable data outputs.

• Establish best practices for screening data integrity, reproducibility, and insight generation.

• Serve as the primary point of contact for cross-functional partners, ensuring timely and accurate delivery of materials.

• Work closely with the Director of Sample Management to communicate capacity, risks, and resource needs.

• Support onboarding and ramp-up of new staff and technologies within the laboratory.

Required Skills, Experience and Education:

• Bachelor’s degree in Chemistry or a related field with a minimum of 8 years of relevant experience, Master’s degree with at least 6 years of relevant experience, or Ph.D. with a minimum of 5 years of relevant experience; a combination of direct and related industry experience is preferred.

• Demonstrated success leading and developing multidisciplinary teams, including experience managing sub-team leads or senior contributors.

• Extensive experience designing, implementing, and operating HTS platforms in collaboration with automation and software engineering teams.

• Direct, hands-on expertise in screening data analytics, including statistical analysis, controls, longitudinal data analysis, and experimental design.

• Experience applying or evaluating machine learning or AI-driven approaches to screening strategy and data interpretation.

• Strong systems-level thinking with the ability to balance innovation with pragmatic delivery of results.

• Excellent written and verbal communication skills.

Preferred Skills:

• Experience shaping or modernizing screening platforms in a growing discovery organization.

• Familiarity with scalable data and analytics frameworks for screening and discovery.

• Oncology or small-molecule drug discovery experience. 

  #LI-Hybrid  #LI-LN1

The Opportunity:

The Associate Director, Integrated Research Management provides leadership across Research disciplines to strengthen operational execution, organizational clarity, and cross-functional alignment within the Discovery organization. This role translates Research strategy into actionable discipline-level plans and drives operational effectiveness within and across scientific teams.

The Associate Director partners with Research leadership to identify business and scientific challenges, improve operating frameworks, and implement scalable solutions that support Discovery performance and organizational growth.

• Translate Research strategy into clear operating plans and drive disciplined execution across Discovery workflows.

• Establish and enforce operational objectives, processes, and decision frameworks that improve cross-disciplinary coordination and execution performance.

• Diagnose and resolve complex operational challenges, removing friction and strengthening clarity, speed, and accountability in program advancement.

• Lead and implement change initiatives within Research, ensuring stakeholder alignment and sustained adoption of improved processes and digital tools.

• Partner directly with senior leadership and cross-functional teams to align priorities and drive operational outcomes.

• Lead and develop the Research Strategic & Administrative Operations discipline, setting performance expectations and fostering a culture of accountability and execution excellence.

Required Skills, Experience and Education:

• Master’s degree in a Life Sciences discipline and at least 8 years’ experience in the biotech/pharmaceutical industry with at least 3 years’ direct experience in Research Operations.

• Significant experience within pharmaceutical or biotechnology Discovery-stage research environments.

• Recognized expertise in Research operations within a laboratory-based scientific setting.

• Demonstrated ability to translate strategic direction into operational execution within matrixed scientific organizations.

• Strong systems thinking and organizational assessment skills.

• Experience working within scientific research platforms (e.g., ELN, LIMS) and familiarity with digital tools that support Research workflows.

• Working awareness of AI-enabled capabilities relevant to Research operations and ability to identify practical applications.

• Proven people leadership experience and ability to influence across disciplines.

Preferred Skills:

• MBA or PhD in Life Sciences, is a plus.

• Experience in Oncology therapeutic area working with small molecules is strongly preferrable.

• Experience transforming and harmonizing complex scientific datasets to enable advanced analytics or AI/ML applications (e.g., data modeling, metadata standards, feature engineering).

• Familiarity with modern data lake/lakehouse architectures and cloud-based research data platforms.

  #LI-Hybrid  #LI-LN1

The Opportunity:

We are seeking a Principal Machine Learning Scientist to lead the development of advanced machine learning approaches that accelerate small-molecule drug discovery. This role sits at the intersection of data science, chemistry, and biology, transforming complex scientific datasets into predictive models that guide target discovery, compound design, and translational hypotheses.

Working closely with experimental scientists, the Principal ML Scientist will develop cutting-edge modeling approaches that integrate chemical, biological, and phenotypic data. The successful candidate will play a key role in advancing a data-driven discovery strategy by designing predictive models, deploying innovative algorithms, and translating insights into actionable decisions that improve the speed and success of the discovery of medicines for patients with RAS-driven cancers.

Key responsibilities include:

Scientific Leadership:

• Define and lead machine learning strategies that accelerate early-stage drug discovery.

• Identify opportunities where AI and advanced analytics can meaningfully improve scientific decision-making.

• Drive the adoption of innovative modeling approaches within multidisciplinary discovery teams.

Model Development:

• Develop predictive models for:

  • Compound activity, selectivity, ADME/Tox, and developability properties.

  • Target engagement, mechanism-of-action, and phenotypic datasets.

Apply modern ML techniques such as:

• Graph neural networks.

• Deep learning for molecular representation.

• Generative chemistry models.

• Active learning frameworks for experimental design.

Cross-Functional Collaboration:

• Partner with medicinal chemists to guide compound design and optimization.

• Work with biologists to interpret complex experimental datasets and generate mechanistic hypotheses.

• Collaborate with data scientists and engineers and ML engineers to deploy models into scalable discovery workflows.

Data Integration:

• Integrate heterogeneous datasets including:

• Chemical structure and screening data.

• Imaging and phenotypic screening data.

• Structural biology and molecular simulation outputs.

Required Skills, Experience and Education:

• PhD in machine learning, computational chemistry, computational biology, computer science, or a related quantitative discipline.

• 8+ years experience applying machine learning or advanced analytics to scientific problems.

• Demonstrated experience working with chemical or biological datasets in drug discovery or related domains.

• Strong expertise in:

  • Python-based ML ecosystems (PyTorch, TensorFlow, scikit-learn).

  • Data analysis and scientific computing (NumPy, Pandas).

  • Deep learning and representation learning techniques.

• Strong understanding of early-stage drug discovery workflows.

• Ability to translate biological or chemical questions into computational frameworks and predictive models.

• Proven ability to communicate complex computational insights to.

• Passion for scientific innovation and a relentless commitment to improving patient outcomes.

Preferred Skills:

• Proven track record of applying advanced AI/ML approaches (deep learning, generative modeling, structure-based ML) to drug discovery or related life sciences domains.

• Experience with cheminformatics or bioinformatics toolkits is highly desirable.

• Familiarity with cloud computing and scalable ML workflows is a plus

• Ability to work at the interface of computational and experimental science. 

  #LI-Hybrid  #LI-LN1