Lead Computational Protein Engineer: Antibody Binder Design

Tom Trese Networking; Rockville (MD); On-site; USD 140,000 - 210,000; Full time

Job summary

Tom Trese Networking in Rockville, MD seeks a Computational Protein Engineer to lead hands-on antibody and protein engineering programs while leveraging computational tools to accelerate design cycles.

The ideal candidate will drive expression, purification, and characterization workflows, design binding and functional assays, and collaborate with structural biologists and computational scientists to integrate insights into experimental programs.

Qualifications

• PhD in biochemistry, molecular biology, protein biochemistry, or related discipline plus 2+ years of industry experience.
• Hands-on expertise in protein expression, purification, and characterization.
• Experience with antibody engineering formats including bispecifics, nanobodies, or related modalities.
• Peer-reviewed publications and/or patents in antibody or protein engineering.
• Familiarity with computational design tools (AlphaFold, Rosetta, ProteinMPNN) a plus.
• Proven ability to lead technical teams and manage complex, multi-faced projects.
• Outstanding written and verbal communication skills.

Responsibilities

• Lead experimental design and execution of antibody and protein engineering programs.
• Drive protein expression, purification, and characterization workflows.
• Design and execute binding and functional assays to evaluate engineered proteins.
• Collaborate with structural biologists and computational scientists to integrate structural insights.
• Apply computational tools such as AlphaFold and Rosetta to support wet lab strategies.

Skills

Protein engineering

Antibody engineering

Team leadership

Scientific communication

Computational design

Education

PhD in biochemistry or related field

Tools

AlphaFold

Rosetta

ProteinMPNN

Job description

Tom Trese Networking in Rockville, MD seeks a Computational Protein Engineer to lead hands-on antibody and protein engineering programs while leveraging computational tools to accelerate design cycles.

The ideal candidate will drive expression, purification, and characterization workflows, design binding and functional assays, and collaborate with structural biologists and computational scientists to integrate insights into experimental programs.