Senior Software Engineer – AI BioDesign

Jobtailor; Seattle (WA); On-site; USD 140,000 - 210,000; Full time

Job summary

Jobtailor is seeking a senior ML/ AI infrastructure engineer to build and maintain ML training and evaluation pipelines across biological modalities. You will design scalable data storage and access infrastructure, and help maintain a model zoo of relevant models.

You will collaborate with leadership to weigh build-vs-buy options for the training and data stack, and participate in lab-in-the-loop training processes. Expect potential collaboration with AI scientists and conference involvement.

Qualifications

• Requires Bachelor’s degree or equivalent and 5+ years of relevant experience.
• Experience running/evaluating ML models and building data access APIs.
• Familiarity with build processes and CI, such as GitHub Actions.
• Preferred: AI models deployment in biological/scientific settings.
• Preferred: Experience with build-vs-buy decisions.
• Preferred: Experience optimizing models for inference and debugging ML training pipelines.

Responsibilities

• Build and maintain ML training and evaluation pipelines for ML models across multiple biological modalities.
• Design data storage, versioning, and access infrastructure for large-scale, in-house data.
• Develop and maintain a model zoo of relevant models.
• Collaborate with leadership on build-versus-buy decisions for the training and data tech stack.
• Develop processes to support a lab-in-the-loop training paradigm.
• Potentially train and develop novel models alongside AI scientists.
• Attend and participate in national and international conferences as appropriate.

Skills

ML model evaluation

Data access API development

CI/CD GitHub Actions

Model optimization for inference

Debugging ML training pipelines

Education

Bachelor's degree

Tools

GitHub Actions

Job description

• Build and maintain ML training and evaluation pipelines for ML models across multiple biological modalities
• Design data storage, versioning, and access infrastructure for large-scale, in-house data
• Develop and maintain a model zoo of relevant models
• Work with the director to make build-versus-buy decisions on the training and data technical stack
• Develop processes to support a lab-in-the-loop training paradigm
• Potentially train and develop novel models alongside AI scientists
• Attend and participate in national and international conferences as appropriate

Requirements

• Bachelor's degree; or equivalent combination of education and experience
• 5 years of relevant experience
• Experience running/evaluating ML models and building data access APIs
• Familiarity with build processes and CI, such as GitHub Actions
• Preferred: Experience deploying AI models in biological or scientific settings
• Preferred: Experience making build versus buy technical decisions
• Preferred: Some experience optimizing models for inference
• Preferred: Some experience debugging ML training pipelines
• Must perform onsite work for the majority of working hours
• Any remote work must be performed in Washington State

Core Competencies

Demonstrates expertise in building and maintaining machine learning training and evaluation pipelines, with a strong focus on data storage and access infrastructure for large-scale biological data. Proficient in making technical decisions regarding model deployment and optimization in scientific settings.

Highest-signal resume keywords

• Machine Learning Model Evaluation
• Data Access API Development
• CI/CD Processes (GitHub Actions)
• Model Optimization for Inference
• Debugging ML Training Pipelines

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Data Storage Design
• Model Zoo Development
• Training Pipeline Development
• Build Versus Buy Decision Making

Industry Keywords

• Biological Modalities
• Lab-in-the-Loop Training
• AI Model Deployment
• Scientific Settings
• Onsite Work Requirement