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Every immune system has a story to tell; the key is knowing how to listen.
Senior Machine Learning Scientist
Location
United States
Posted
88 days ago
Salary
$144.6K - $217K / year
Seniority
Senior
Job Description
Senior Machine Learning Scientist
Adaptive Biotechnologies Corp.
• Design, implement, train, and iterate on novel deep learning models for TCR–pMHC specificity prediction. • Extend and adapt advances in protein language models, structure prediction, generative modeling, and representation learning to the immune receptor setting. • Utilize scalable training infrastructure to support large-scale model development and experimentation. • Conduct rigorous benchmarking and evaluation strategies to ensure models are scientifically sound and practically superior. • Translate biological principles of T cell recognition into principled modeling decisions. • Influence large-scale experimental data generation to maximize modeling leverage and long-term performance gains. • Provide input and technical recommendations to broader modeling discussions and roadmap planning. • Work closely with computational biology, immunology, translational, and engineering teams to ensure models are robust, reproducible, and aligned with overall product goals. • Communicate modeling insights, approaches, and results to cross-functional scientific audiences. • Contribute to publications, presentations, etc. through technical execution and analysis.
Job Requirements
- PhD in a quantitative discipline (e.g. Machine Learning, Computational Biology, Computer Science) + 5 years progressive experience applying machine learning to real-world scientific or biological problems OR equivalent combination of education and experience.
- Progressive experience in the development and deployment of deep learning methods
- Strong hands-on experience in python and modern ML tooling (PyTorch preferred)
- Strong experience in deep learning architecture design and implementation.
- Experience working with large datasets and high-performance computing environments
- Ability to independently define, scope, and execute complex technical research problems.
- Strong written and verbal communication skills, with ability to present highly technical material to diverse audiences.
- Demonstrated ability to collaborate effectively in cross-functional, multi-disciplinary teams
- Driven by impact: motivated to see models transition from research to clinical and commercial application.
Benefits
- equity grant
- bonus eligible
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Summary: Join Temporal, a leading Solana native Research & Development firm, as a Machine Learning Engineer. Contribute to cutting-edge projects by designing, training, and deploying predictive models for adversarial systems while building high-performance ML infrastructure. Stay at the forefront of technological advancements by engaging with the latest research and contributing to open-source development. Responsibilities: - Design, train, and deploy predictive models for adversarial systems - Build and maintain high-performance ML infrastructure that scales from research prototypes to production systems - Stay on the frontier: read new papers, reproduce results, experiment with hardware, and contribute to open source Required Skills: - Deep foundations in optimization, statistics, and linear algebra - Expertise in ML frameworks (ideally PyTorch) and strong software engineering skills - Experience building real-time or distributed ML systems - Curiosity, independence, and a track record of original contributions (research, open source, competitions, patents) Compensation: $200,000–$300,000 base salary, equity package, and discretionary bonus
• Responsible for designing, building, integrating, optimizing, and maintaining AI-powered applications that leverage generative models • Collaborate closely with teammates as they develop and deliver user stories while supporting AI-powered products as they evolve • Design and implement applications using large language models (LLMs) and other generative models to embed intelligent capabilities directly into software products • Activities may include prompt engineering, model integration, building Retrieval-Augmented Generation (RAG) pipelines, and developing scalable AI services • Interact with business stakeholders, infrastructure teams, and development teams to ensure business requirements are effectively addressed through generative AI solutions • Support evaluation, performance optimization, testing, and monitoring of AI systems in production • Work with domain data, improving prompts and AI workflows, and creating documentation or enablement materials for generative AI solutions • Able to work independently with minimal guidance, while collaborating with cross-functional teams of varying skill levels • Review submitted code and prompt implementations, providing feedback and improvements based on engineering and responsible AI best practices • Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions • Documents, reviews, and ensures that all quality and change control standards are met • Writes custom code or scripts to automate infrastructure, monitoring services, and test cases • Writes custom code or scripts to do 'destructive testing' to ensure adequate resiliency in production • Configures commercial off the shelf solutions to align with evolving business needs • Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively
• Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions • Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable • Configures commercial off the shelf solutions to align with evolving business needs • Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively • Participates in learning activities around modern software design, machine learning, and development core practices (communities of practice) • Proactively views articles, tutorials, and videos to learn about new technologies and best practices being used within other technology organizations • Attends conferences and learns how to apply new innovations and technologies where appropriate • Researches and analyzes business trends and behavioral data to identify opportunities for improvement and new initiatives • Leads the evaluation development and recommendation of specific technology products and platforms to provide cost-effective solutions that meet business and technology requirements • Researches and designs best fit infrastructure, network, database, security, and machine learning architectures for products • Proactively creates and maintains tools for monitoring and support • Participates in project planning and management across multiple efforts • Develops formal training courses • Fields questions from other product teams or support teams • Monitors tools and participates in conversations to encourage collaboration across product teams • Provides application support for software running in production • Proactively monitors production Service Level Objectives for products • Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality
• Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions; • Documents, reviews, and ensures that all quality and change control standards are met; • Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable; • Writes custom code or scripts to automate infrastructure, monitoring services, and test cases; • Writes custom code or scripts to do 'destructive testing' to ensure adequate resiliency in production; • Program configuration/modification and setup activities on large projects using HD approved methodology; • Configures commercial off the shelf solutions to align with evolving business needs; • Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively • Participates in learning activities around modern software design, machine learning, and development core practices (communities of practice); • Proactively views articles, tutorials, and videos to learn about new technologies and best practices being used within other technology organizations • Fields questions from other product teams or support teams; • Monitors tools and participates in conversations to encourage collaboration across product teams; • Provides application support for software running in production; • Proactively monitors production Service Level Objectives for products; • Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality
