Company Overview:

1859 is on a mission to accelerate drug discovery and deliver the next generation medicines to patients faster. Our activity-based pico-scale small molecule screening engine is uniquely integrated with generative AI models to streamline de novo drug design for both novel and well-validated targets with unmet needs. Iterative design-make-screen cycles are powered by multi-parametric molecule optimization models trained on our large experimental data sets from validated libraries in addition to conventional public data. Our engine yields data that are rich with SAR trends in desired properties that enable our experienced team of scientists to test fewer compounds faster and quickly prioritize novel selective lead compounds for conversion into clinical candidates, unlocking new therapeutic opportunities faster for patients who are waiting.

We are comprised of high-performance engines, not cogs, and we invest in our people! Benefits include: paid holidays, flexible PTO, medical, dental and vision insurance, a 401(k) plan, and equity incentives. We are committed to training and career development for our people. Our goal is to provide a collaborative, data driven, productive and highly efficient company culture.

If you have a passion for changing the world of new medicine discovery, we encourage you to apply!

Position Summary:

We are looking for a Principal Machine Learning Scientist to join our AI/ML Team. The team designs and implements predictive and generative models, as well as the software that supports our library design, hit selection, hit-to-lead and lead optimization pipeline.

Key Responsibilities:

• Collecting, curating, and analyzing bioactivity and ADMET data from internal screening programs and public and commercial sources
• Designing, identifying, implementing, and validating machine/deep learning and AI model architectures and tools
• Applying models to large datasets and reasoning about the outcomes
• Using models to screen and design combinatorial chemical libraries for a diverse set of protein targets across different disease areas
• Building an automated pipeline to refresh existing models with new data
• Building dashboards that monitor model predictive performance over time
• Keeping up with the cutting edge of ML/AI research, especially in fields related to deep learning, generative models, reinforcement learning, active learning, etc.
• Implementing internal and/or published ideas and validating them in a timely mannerCommunicating results to internal and external stakeholders

Requirements:

• Ph.D. in Computer Science, Mathematics, Statistics, Computational Chemistry/Physics, or a related quantitative discipline
• 5+ years of relevant experience
• Expert applied machine learning skills, demonstrated by a strong publication record at leading conferences, journals, or other substantial contributions to the field
• Solid analytical and statistical skills
• Scientific rigor, healthy skepticism, and detail-oriented in developing and analyzing machine learning models
• Expert knowledge of the Python machine learning frameworks, e.g., Scikit-learn, PyTorch or TensorFlow
• Comfortable with Linux command-line environmentPro-active team player and creative problem solver

Highly Desirable:

• Experience with one or more areas of geometric deep learning, including generative models, and reinforcement learning
• Familiarity with open challenges in AI-driven drug discovery
• Ability to deliver production-quality code; experience with code review
• Experience with:
  • DEL-based HTS
  • Cloud computing environments (AWS/Azure/GCE)
  • Creating scalable methods that utilize vast amounts of data

Compensation:

$150,000 - $225,000. Actual base pay within this range will be determined by several factors, including but not limited to, relevant experience, skills, qualifications, location, and other job-related factors permitted by law.

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We take pride in fostering an environment where all team members are afforded the freedom to pursue their passion without regard to race, color, religion, national or ethnic origin, gender, sexual orientation, gender identity or expression, age, disability, veteran status or any other characteristics protected by law.