СШАScientist - Chemistry
2 мес. назад
СШАSeniorOnsite
organic synthesismedicinal chemistryautomationmachine learningrobotics
Contribute to drug discovery by designing and executing organic syntheses, supporting hit-to-lead and lead optimization, and collaborating on automation and machine learning integration.
О компании
- At , our mission is to help the world discover new medicines faster—and build the capacity to do it here in America. We bring AI, robotics, and life sciences together in automated labs that design, run, and learn from experiments, with each experiment making the next one better. We’re not developing drugs of our own. We’re building the infrastructure for the people who do.
- Our organization combines synthetic chemistry expertise with robotics and automation: Our long term vision is to build an autonomous synthetic chemistry lab that delivers small-molecule discovery chemistry services to pharmaceutical and biotechnology clients. We are seeking a Scientist to join our Medicinal Chemistry group and contribute directly to client drug discovery programs.
- The successful candidate will design and execute multi-step organic syntheses, propose and evaluate synthetic routes, and generate high-quality compounds and data in support of hit-to-lead and lead optimization campaigns.
- Beyond bench chemistry, this role plays a key part in our automation mission: the compounds, reactions, and characterization data generated will help build structured, high-quality training datasets for machine learning models, and the chemist will work directly with AI scientists and roboticists to translate synthetic know-how into automated and self-optimizing chemistry platforms. This is a hands-on role requiring strong technical execution, sound chemical judgment, and comfort operating at the interface of traditional bench chemistry and emerging lab automation.
Обязанности
- Design, plan, and execute multi-step organic synthesis of target compounds in support of client medicinal chemistry and hit-to-lead/lead optimization programs.
- Propose and evaluate synthetic routes, including retrosynthetic analysis, selection of viable pathways, and identification of scalable, cost-effective alternatives.
- Perform reaction optimization (yield, selectivity, purity, scalability) and troubleshoot synthetic problems using sound mechanistic reasoning.
- Purify and characterize intermediates and final compounds using standard techniques (column chromatography, prep-HPLC, recrystallization) and analytical methods (LCMS, NMR, HPLC, HRMS).
- Accurately and promptly record experimental procedures, observations, and results in electronic laboratory notebooks (ELN) in accordance with company and client documentation standards.
- Support scale-up of key intermediates and final compounds as required by project needs.
- Communicate progress, data, and route strategy clearly and regularly to project leaders and, where applicable, directly to client scientists.
- Generate clean, well-structured experimental data (reaction inputs/outputs, conditions, yields, analytical results) suitable for use as high-quality training data for machine learning and reaction-prediction models.
- Partner closely with AI scientists and roboticists to translate synthetic routes and bench protocols into procedures executable on automated/robotic synthesis platforms, providing chemistry expertise during platform design, testing, and validation.
- Provide hands-on feedback on automated workflows (liquid handling, automated reaction setup, in-line analytics) and help troubleshoot discrepancies between manual and automated execution of chemistry.
- Support iterative improvement of self-driving lab and automated chemistry capabilities by flagging edge cases, failure modes, and data quality issues encountered at the bench.
- Maintain compliance with laboratory safety protocols, chemical hygiene procedures, and proper handling/disposal of hazardous materials.
- Manage time and priorities effectively across multiple concurrent projects and client deadlines.
- Contribute to continuous improvement of synthetic methods, workflows, and best practices within the chemistry group.
Требования
- B.S. or M.S. in Chemistry, Organic Chemistry, Medicinal Chemistry, or a related discipline.
- 0–3 years of relevant synthetic organic chemistry experience (industry, CRO, or advanced academic/graduate research); internship or co-op experience considered for entry-level candidates.
- Demonstrated hands-on experience with multi-step organic synthesis and standard purification techniques.
- Working knowledge of retrosynthetic analysis and route design principles.
- Proficiency interpreting spectroscopic and analytical data (NMR, LCMS, HPLC) to confirm structure and purity.
- Solid understanding of laboratory safety practices and safe handling of reactive/hazardous chemicals.
- Strong record-keeping and documentation habits; experience with ELN systems is a plus.
- Excellent organizational skills with the ability to manage multiple projects and shifting priorities in a deadline-driven environment.
- Effective written and verbal communication skills, with the ability to present data clearly to project teams.
- Primarily laboratory-based role requiring extended periods standing at the bench and working with standard synthetic chemistry equipment.
- Regular exposure to laboratory chemicals, solvents, and reagents; strict adherence to PPE and safety protocols is required.
- Ability to lift and move laboratory supplies and equipment (up to approximately 25 lbs) as needed.
Будет плюсом
- Prior CRO, pharmaceutical, or biotech industry experience supporting drug discovery programs.
- Experience with parallel synthesis, medicinal chemistry SAR exploration, or DMPK-informed compound design.
- Familiarity with chemistry software tools (e.g., ChemDraw, electronic notebooks, reaction database/search tools).
- Experience with air- and moisture-sensitive chemistry, flow chemistry, or automated/robotic synthesis platforms.
- Interest or prior experience in AI/ML-driven chemistry, cheminformatics, or self-driving laboratory concepts, and enthusiasm for collaborating with AI scientists and roboticists on lab automation.
- Comfort working with structured data entry and an appreciation for how data quality and consistency affect downstream machine learning model performance.
- Track record of authorship or contribution to publications, patents, or internal technical reports.