About Us
Soley Therapeutics is a biotechnology company that was created with the belief that drug discovery and development should be faster and less expensive, with a much higher probability of success. To achieve this goal, we are pioneering a novel, fully integrated approach that combines data and machine learning insights at every step of the process. We are a multi-disciplinary team that brings together experts in drug development, data engineering, and machine learning to create a cohesive platform. Our end goal is to create life-changing medical treatments by combining expertise in technology and life sciences with a comprehensive view of the entire drug discovery and development process.
Soley Therapeutics is committed to hiring a world-class team that brings together a wide variety of different skills and experiences. We are committed to inclusion across race, gender, age, religion, identity, and experience, and believe that diversity makes us stronger by bringing in new ideas and perspectives. We strive to create a workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.
About the Role
We are seeking a Machine Learning Lead to design and implement advanced ML solutions that unlock insights from diverse phenotypic and molecular datasets—accelerating the discovery of life-saving therapeutics. We are looking for mission-driven individuals that are eager to bring ideas from conception to reality.
At Soley, Machine Learning Engineers will work side-by-side with Software engineers, Data Scientists, Data Engineers and Drug Development Scientists to achieve drug discovery objectives.
This is a hands-on leadership role for a mission-driven individual who thrives on turning ideas into reality. You will work closely with software engineers, data scientists, data engineers, and drug development scientists to integrate ML into every facet of our drug discovery pipeline.
As the Machine Learning Lead, you will:
- Develop novel ML algorithms and architectures to model complex biological systems.
- Translate state-of-the-art ML techniques into the domains of phenotypes and chemical compounds.
- Drive engineering excellence and set high standards for software craftsmanship and process.
What You’ll Do…
- Developing new machine learning algorithms and architectures to model complex biological systems using traditional and modern learning techniques.
- Design algorithms for segmentation, feature extraction, clustering and classification of biological images and meta data.
- Translating state-of-the-art machine learning techniques designed to work on images, text, or audio into the domain of phenotypes and chemistry compounds.
- Creating a large-scale distributed training and hyperparameter optimization system.
- Using data mining and processing techniques to uncover new sources of data and clean our existing datasets.
- Using predictive modeling to make critical decisions about which tests to run in the lab.
- Supporting in-house experimental assay development and combining data from multiple internal and external data sources into a universally useful and accessible data platform.
- Collaborate with scientific and engineering leaders to develop engineering requirements, development roadmaps, project plans and work priorities.
- Be able to work independently, collaborate as needed with team members, take ownership of assigned projects and set and keep a high-quality engineering bar for all projects.
What You Bring…
- Education: Advanced Degree in Computer Science, or a Data Related field focused on ML.
- Deep Learning: Experience building CNNs, RNNs, GANs, etc. from scratch in a modern deep learning framework (TensorFlow, PyTorch, Caffe, etc.).
- Classical ML: Experience with non-neural network deep learning models (random forests, SVM, etc.) and basic statistics.
- Experience with transformers and diffusion models.
- Cloud Computing: Knowledge of cloud computing platforms, such as AWS and OCI, and experience setting up and managing cloud computing and storage resources.
- Data Engineering: Experience building large-scale data processing pipelines feeding into and analyzing results from ML.
- Industry experience: 5+ years of experience in industry after completing advanced degree conducting applied research using deep learning and classical ML for computer vision applications, including bringing systems to production.
- Programming: Strong coding skills and experience with Python and AI/ML libraries.
- Leadership & Communication: Ability to influence cross-functional teams and communicate complex ideas clearly.
- Strategic thinker with deep business experience leading and executing partnerships across pharmaceutical and technology sectors.
- Proven ability to generate novel, defensible patent concepts, stay ahead of emerging AI/ML technologies, and closely track competitive activity in AI‑enabled drug discovery.
- Mindset: Curious, adaptable, and eager to collaborate in a fast-paced environment. Execution is a must.
You May Also Bring…
- Experience with MLOps and DevOps and supporting large scale machine learning projects in industry or academic settings.
- Publications and presentations at ML conferences and journals.
- A background in drug development or another healthcare or biotechnology field.
