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We are seeking an AI Scientist to develop novel algorithms and machine learning architectures that accelerate the drug discovery and delivery process. This is a high-impact, cross-functional role that involves close collaboration between lab scientists, software engineers, and business leaders.
You will not only design and build foundational models but also work directly with wet lab teams to translate biological challenges into computational solutions. Your work will have a direct, tangible impact on the end-to-end development process.
Key Responsibilities
Develop foundational generative models for designing novel chemical structures and compounds for drug and vaccine delivery systems.
Design, build, and scale production-grade machine learning models.
Contribute to building and growing the computational and machine learning team.
Collaborate closely with the wet lab and platform teams to improve and iterate on therapeutic applications.
Establish and maintain best practices for model development, validation, and deployment.
Communicate findings and progress to technical and non-technical stakeholders, including leadership.
What We’re Looking For
You may thrive in this role if you have:
A Master’s or PhD in Computer Science, Machine Learning, or a related field.
2+ years of experience building and deploying production machine learning models.
Advanced proficiency in Python.
Experience with machine learning frameworks like PyTorch, TensorFlow, or JAX.
Strong mathematical foundations and a deep understanding of machine learning principles, network architectures, evaluation metrics, and optimization techniques.
Solid background in software engineering and algorithm design.
The ability to thrive in fast-paced, dynamic environments with evolving objectives.
Passion for solving end-to-end problems and driving complex technical solutions.
Interest in computational chemistry, biology, and healthcare.
Nice to Have
Experience applying machine learning to biological or chemical domains.
Familiarity with state-of-the-art generative models (e.g., LLMs, diffusion & flow models, VAEs, GANs, EBMs).
Publications at top-tier conferences such as NeurIPS, ICML, ICLR etc.
Familiarity with wet lab processes and experimental pipelines.
Location
Open to remote
HQ in Philadelphia.
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We are seeking a Senior Scientist with deep expertise in computational and machine learning–driven protein binder design to join our growing scientific team. This role is ideal for a PhD-level scientist who thrives at the interface of sequence/structure-based protein design and experimental validation, and who wants their work to directly influence therapeutic discovery programs.
In this role, you will develop and apply advanced computational approaches to design and optimize protein binders, working closely with experimental scientists to translate models into testable molecules and iterate based on functional data. You will play a central role in shaping Kosha’s binder design capabilities and advancing internal discovery platforms.
Key Responsibilities
Lead independent computational research focused on protein and binder design for therapeutic applications.
Develop, implement, and refine sequence- and structure-based models to guide binder discovery and optimization.
Deliver robust, reusable computational methods and workflows that support iterative experimental testing.
Collaborate closely with experimental scientists to ensure designs are aligned with biological constraints and assay capabilities
Interpret experimental results and incorporate feedback into subsequent design cycles.
Communicate scientific insights, limitations, and progress clearly to cross-functional teams and company leadership.
Contribute to building internal best practices for computational protein design, data management, and model evaluation.
What We’re Looking For
PhD in computational biology, computational chemistry, machine learning, computer science or a closely related field, with a strong focus on biological or chemical systems.
Demonstrated experience in developing novel AI, including sequence- and/or structure-based approaches for biomolecular design.
Strong foundation in protein structure, molecular interactions, stability, and biophysical constraints.
Hands-on experience with modern protein design and modeling tools (e.g., AlphaFold or related structure predictors, ProteinMPNN, Rosetta-based workflows, diffusion or generative models for proteins).
Proficiency in Python and experience using modern ML frameworks (e.g., PyTorch, TensorFlow, or JAX).
Ability to work independently while contributing effectively within a highly collaborative, interdisciplinary team.
Nice to HavePrior industry experience applying computational methods to therapeutic protein or binder discovery.
Familiarity with integrating computational outputs into wet-lab workflows.
Strong scientific communication skills, including the ability to translate complex models into actionable design decisions.
Experience working in fast-paced startup or research environments with evolving priorities.
Track record of publications or preprints in protein engineering, structural biology, or computational biology.
Location
Open to remote
HQ in Philadelphia.
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We are seeking a Platform Software Engineer to build robust infrastructure and tools that power our drug discovery and delivery platform. This is a high-impact, cross-functional role that involves close collaboration between lab scientists, machine learning engineers, and business leaders.
You will design and build scalable systems that enable our computational and experimental teams to work efficiently. Your work will directly support the end-to-end drug development process, from initial discovery through preclinical development.
Key Responsibilities
Design, build, and maintain production-grade software platforms and infrastructure for computational drug discovery workflows
Develop internal tools and APIs that enable lab scientists and ML engineers to interact with data, models, and experimental results
Build data pipelines and infrastructure to support large-scale biological and chemical datasets
Architect and implement scalable, reliable systems that integrate computational models with experimental workflows
Establish and maintain best practices for software development, testing, deployment, and monitoring
Collaborate closely with wet lab, machine learning, and platform teams to understand needs and deliver solutions
Communicate technical decisions and progress to both technical and non-technical stakeholders, including leadership
What We're Looking For
A Bachelor's or Master's in Computer Science, Software Engineering, or a related field
2+ years of experience building and deploying production software systems
Advanced proficiency in Python and at least one other programming language (e.g., Go, Rust, Java, TypeScript)
Experience with cloud platforms (AWS, GCP, or Azure) and container orchestration (Docker, Kubernetes)
Strong understanding of software architecture, design patterns, and distributed systems
Solid background in databases, APIs, and data engineering
The ability to thrive in fast-paced, dynamic environments with evolving objectives
Passion for solving end-to-end problems and building tools that empower scientists
Interest in computational biology, chemistry, and healthcare applications
Nice to Have
Experience building platforms or tools for scientific computing, bioinformatics, or computational chemistry
Familiarity with machine learning infrastructure and MLOps practices
Knowledge of data engineering frameworks (e.g., Airflow, Dagster, Prefect)
Experience with scientific Python stack (NumPy, Pandas, SciPy) or visualization tools
Understanding of laboratory information management systems (LIMS) or electronic lab notebooks (ELN)
Contributions to open-source scientific software projects
Location
Open to remote
HQ in Philadelphia