Staff AI/ML Engineer – Controllable Biology

Jobtailor; California (MO); On-site; USD 180,000 - 240,000; Full time

Job summary

Jobtailor is seeking an experienced ML/AI engineering leader to convert complex biological questions into mathematical problems and deliver end-to-end AI/ML solutions. You will mentor cross-functional teams, drive quarterly outcomes, and demonstrate transparent communication with leadership.

You will champion best practices in ML, software engineering, and model safety while balancing research velocity and operational reliability across biology-focused data pipelines.

Qualifications

• Master’s degree in a related field; PhD a plus.
• 7+ years with deep learning algorithms and scalable training.
• 7+ years in software development with Agile delivery.
• 5+ years in technical lead or engineering management with direct reports.
• 7+ years with Python and PyTorch.
• Experience with biological sequence and network data.

Responsibilities

• Convert complex biological questions into tractable mathematical problems.
• Decompose large problems into quarterly measurable results and deliver them through sprints.
• Mentor multidisciplinary engineering team members and guide delivery.
• Demo work early and often balancing research and velocity.
• Champion ML, software engineering, code review, and best practices.
• Define technical roadmaps for AI/ML products balancing safety and reliability.
• Communicate transparently with leadership and organization.
• Ensure models are explainable, safe, and relevant with stakeholders.
• Deliver end-to-end AI/ML solutions from data sourcing to services.

Skills

Machine Learning Algorithms

Python Programming

PyTorch Framework

Software Development Best Practices

Technical Leadership

Biological Sequence Data

Genomics

Proteomics

Agile Methodologies

Mentoring

Communication

Collaboration

Task Delegation

Transparency

Education

Master’s degree in a related field

PhD in Quantitative Field

Tools

CI/CD Pipelines

Job description

Convert complex biological questions into tractable mathematical problems solvable by modern computational methods
Decompose large problems into quarterly measurable results and deliver them through engineering sprints
Mentor multidisciplinary engineering team members, delegate tasks, and guide delivery
Demo work early and often while balancing research and engineering velocity
Champion machine learning, software engineering, code review, and agentic development best practices
Define technical strategies and roadmaps for AI/ML products, balancing scientific needs, safety, and operational reliability
Communicate transparently and accurately with leadership and the broader organization
Partner with stakeholders to ensure models are explainable, safe, and relevant
Apply machine learning and AI methods to biological networks and sequence data from large-scale human genetic, functional genomic, and single-cell experiments
Independently deliver end-to-end AI/ML solutions, from training-data sourcing and model design through benchmarking, tested code, and performant services

Requirements

• Master’s degree in a related field, such as computer science, mathematics, or natural sciences
• 7+ years’ experience with standard deep learning algorithms, model architectures, machine learning best practices, scalable training, and deployment
• 7+ years’ experience in software development, including code reviews, version control systems, CI/CD pipelines, software testing, technical documentation, and Agile delivery methodologies
• 5+ years’ experience in a technical lead or engineering manager role with direct reports, mentoring software engineers or machine learning practitioners
• 7+ years’ experience with Python and PyTorch, or an equivalent machine learning framework
• Experience working with biological sequence and network data, including genomics, transcriptomics, proteomics, gene regulatory networks, or related biological datasets
• Experience collaborating with stakeholders across multiple teams, functions, and geographic regions
• PhD in a quantitative or computational field is a plus
• Background in modelling disease biology, systems biology, molecular biology, and biochemistry is a plus
• Track record of delivering high-quality, research-level machine learning and robust software solutions is a plus
• Peer-reviewed publications in major AI conferences are a plus

Core Competencies

Demonstrates expertise in applying machine learning and AI methods to biological data, with a strong focus on delivering end-to-end solutions and mentoring engineering teams. Proven ability to define technical strategies and communicate effectively with stakeholders to ensure model relevance and safety.

Highest-signal resume keywords

• Machine Learning Algorithms
• Python Programming
• PyTorch Framework
• Software Development Best Practices
• Technical Leadership

ATS Optimization Keywords

Hard Skills

• Deep Learning Algorithms
• Model Architectures
• Scalable Training
• CI/CD Pipelines
• Software Testing
• Technical Documentation
• Agile Methodologies
• Biological Sequence Data
• Genomics
• Proteomics

Soft Skills

• Mentoring
• Communication
• Collaboration
• Task Delegation
• Transparency

Certifications & Qualifications

• Master’s Degree
• PhD in Quantitative Field

Industry Keywords

• Computational Biology
• Systems Biology
• Molecular Biology
• Biochemistry
• Gene Regulatory Networks