Scientist/Senior Scientist, Protein Engineering

Arbor Biotechnologies, a clinical stage, next-generation gene editing company based in Cambridge, MA, is advancing a portfolio of first-in-class genomic medicines addressing serious diseases for which there are no existing functional cures, with a CNS pipeline initially prioritizing multiple ALS targets and a co-developed, clinical program, ABO-101, for the treatment of primary hyperoxaluria type 1. The company’s unique breadth of gene editing technologies goes beyond the limitations of early editing approaches to unlock access to new gene targets and expand the therapeutic reach of genomic medicines. For more information, please visit: arbor.bio.

You're driven by purpose and curious by nature. You take ownership, speak up, and stay steady when things get hard. You'd rather the team win than get the credit. If that sounds like you, keep reading.

We are seeking a Scientist / Senior Scientist, Protein Engineering to drive experimental execution for assigned protein engineering workstreams within our genome editing programs. This role will apply protein biochemistry, molecular biology, structure-function analysis, and cellular screening approaches to help generate improved genome editing molecules. 

You will independently design and execute experiments within established project goals, evaluate engineered protein mutants and variants, analyze biochemical and cellular data, and translate results into clear recommendations that shape next-step project decisions. You will work closely with computational scientists, experimental teams, and project leads to apply structure-guided and AI-enabled design approaches, interpret results, and support iterative design-build-test-learn cycles. 

This role is ideal for a rigorous and innovative molecular engineer who combines hands-on experimentation with strong data analysis, structural insight, and collaborative problem-solving. 

The successful candidate will contribute technical expertise to project-team decisions and help document and communicate results to support internal technology hand-offs.

·       Drive experimental execution for assigned protein engineering workstreams within established campaign objectives.

·       Design, generate, and evaluate protein mutants and variants to support genome editing programs.

·       Recommend experimental approaches, controls, and follow-up studies in collaboration with project leads and functional partners.

·       Apply protein engineering approaches, such as rational design, structure-guided engineering, deep mutational scanning, or related methods, based on project goals and the enzyme properties being optimized.

·       Independently design and execute experiments to test protein engineering hypotheses and evaluate variant performance.

·       Analyze experimental results, troubleshoot issues, and recommend next experimental steps to support iterative design-build-test-learn cycles.

·       Use scientific judgment to influence project-team decisions through clear, data-driven recommendations.

·       Apply structure-guided protein engineering approaches using experimental structures, homology models, AI-predicted structures, and other relevant models to generate and prioritize variant hypotheses.

·       Use protein structure visualization and molecular modeling tools, such as PyMOL, ChimeraX, and AlphaFold-derived models, to analyze active sites, protein-protein interfaces, conformational states, and sequence-structure-function relationships.

·       Evaluate sequence-structure-function relationships to inform protein design and interpret variant performance.

·       Design and perform biochemical assays to investigate protein properties in support of engineering goals, including activity, stability, specificity, kinetics, or other relevant functional readouts.

·       Interpret biochemical and cellular assay results to support protein engineering decisions and identify appropriate follow-up experiments.

·       Explain scientific and technical findings clearly to project teams and functional partners.

·       Develop, optimize, and execute cell-based screening workflows using transient transfection, nucleofection, transduction, and other relevant approaches in dividing and non-dividing cell models.

·       Improve high-throughput screening workflows to increase experimental throughput, data quality, and breadth of information collected about engineered enzymes.

·       Design, order, and generate reagents, including AAVs, mRNA, gRNAs, plasmids, protein constructs, or other materials needed to evaluate engineering progress.

·       Prepare sequencing libraries and analyze experimental outputs to assess editing outcomes, variant performance, assay quality, and experimental reproducibility.

·       Troubleshoot experimental workflows and propose practical improvements to assay robustness, scalability, and interpretability.

·       Apply sound scientific judgment to choose appropriate methods and interpret data within established project and team practices.

·       Collaborate with computational scientists to apply AI-based design, analysis, modeling, and variant prioritization approaches.

·       Work with project leads and experimental teams to align experimental plans with project goals.

·       Communicate experimental findings, data interpretation, and technical recommendations to project teams and functional partners.

·       Document experimental findings and contribute to patents, publications, technical reports, and regulatory documentation as appropriate.

·       Support technology hand-offs for assigned workstreams by summarizing methods, results, key learnings, technical risks, and recommended next steps.

·       Stay current with relevant technologies in protein engineering, artificial intelligence, computational biology, and structural biology, and share observations that may inform team practices.

·       May provide technical guidance to research associates or junior scientists on specific experimental workflows.

·       PhD in biology, biochemistry, molecular biology, bioengineering, or a related discipline with 2+ years of relevant post-PhD industry or postdoctoral experience in protein engineering, enzymology, molecular biology, protein biochemistry, genome editing, or a related field.

·       Hands-on experience working with nucleic acid binding proteins, such as CRISPR nucleases, polymerases, recombinases, transcription factors, or related systems.

·       Hands-on experience engineering enzymes or proteins to improve function, including kinetics, stability, activity, specificity, expression, delivery, or related properties.

·       Expertise in evaluating sequence-structure-function relationships and applying those insights to experimental design or data interpretation.

·       Hands-on experience with molecular biology, biochemical, or cellular approaches for measuring protein, enzyme, or editing activity.

·       Experience independently designing, executing, troubleshooting, and interpreting complex experiments in protein engineering, enzymology, molecular biology, or protein biochemistry within defined project goals.

·       Ability to analyze experimental data and translate findings into clear technical recommendations for project teams and project leads.

·       Experience contributing to protein engineering experimental planning with evidence of successful execution and project-team impact.

·       Hands-on experience with AAV production and testing, or mRNA generation and testing, in cellular contexts is a plus.

·       Experience using AI design tools for protein engineering is a plus.

·       Experience with CRISPR gene editing technologies is a plus.

·       Familiarity with Illumina sequencing workflows is a plus.

·       Experience collaborating with computational scientists, project leads, or cross-functional research teams is preferred.

·       Detail-oriented and motivated

·       Willingness to learn

·       Flexible mindset

·       Clear and friendly communication

·       Team-first attitude

·       Strong scientific judgment

·       Independent execution

·       Data-driven mindset

·       Collaborative problem-solving

·       Comfort with ambiguity