Senior Scientist, AI-Assisted Lab Optimization | On site - Full Time

Xperteez Technology • Cambridge, MA

21h • Onsite

About The Position

The Senior Scientist will lead a hands-on, AI-in-the-loop wet-lab research and development program within the Detection division. The role uses frontier AI models as collaborative partners to optimize laboratory protocols and generate data and insights relevant to biosafety and biosecurity. The scientist will own the program from experimental design through bench execution, analysis, and documentation, while working with Detection scientists, research associates, and AI, computational, and laboratory science leadership.

The work focuses on structured optimization of viral enrichment, host and background depletion, nucleic acid extraction, and sequencing library preparation. The scientist will explore how AI models can propose and prioritize protocol changes, shape experiments, interpret quality-control and sequencing results, and guide subsequent iterations. They will also build data-grounded QC feedback loops, establish reproducible benchmarks and controls, and design a system for recording AI interactions and their contributions. A qualitative, well-documented case study of AI’s effects on expert wet-lab work will inform AI safety and CBRN threat modeling.

The role also includes maintaining rigorous QA/QC and electronic lab notebook documentation, helping colleagues apply AI tools in their work, and contributing to the laboratory’s automation direction. This is a full-time, in-person position in Cambridge, Massachusetts. The start date is as soon as possible; visa sponsorship is not available.

Requirements

• PhD in molecular biology, microbiology, bioengineering, or a related field, or an MS with equivalent depth of hands-on experience.
• Deep, hands-on wet-lab expertise in relevant protocols, including viral enrichment, nucleic acid extraction, and NGS library preparation.
• Protocol development must have been a substantive focus of the candidate’s work, rather than an occasional task.
• Demonstrated track record of optimizing and troubleshooting complex molecular protocols.
• Rigorous experimental design and quantitative data analysis skills.
• Strong, current, self-directed fluency with frontier AI tools in scientific work, including the ability to interrogate outputs, recognize experimentally unsound suggestions, and actively calibrate model use.
• Excellent communication and collaboration habits, with the ability to work credibly across teams with different cultures and working rhythms.
• Proven ability to independently own a research program and exercise sound scientific judgment with limited supervision.

Nice To Haves

• Experience with metagenomic sequencing and environmental samples such as wastewater.
• Experience with technically demanding sample types, including soil, leaf litter, marine, and sediment samples.
• Experience working with RNA.
• Background in molecular ecology or environmental microbiology, and a varied research history.
• Familiarity with liquid-handling automation such as Opentrons, Hamilton, or Tecan.
• Working knowledge of bioinformatics and sequencing QC pipelines, and scripting in Python or R for data analysis.
• Prior work in biosecurity, pandemic preparedness, or a related mission-driven setting.
• Background in viral ecology, low-abundance or rare-target detection, targeted sequencing, probe-capture enrichment, or host-depletion chemistries.

Responsibilities

• Lead a hands-on, AI-in-the-loop wet-lab R&D program from experimental design through execution, analysis, and documentation.
• Work with the Detection team to identify structured optimization experiments for viral enrichment, host and background depletion, nucleic acid extraction, and sequencing library preparation.
• Use frontier AI models as collaborative design partners to propose and prioritize protocol modifications, structure experiments, interpret QC and sequencing output, and recommend next iterations.
• Execute bench experiments personally and coordinate experiments across detection scientists and research associates.
• Build QC feedback loops that aggregate historical sequencing reports and protocol metadata so models can make recommendations grounded in internal data as well as literature.
• Benchmark results rigorously using reproducible metrics and controls to establish whether improvements are real and attributable to AI.
• Design a codified system for labeling and annotating AI interactions so their contributions can be examined later.
• Produce a qualitative, well-documented case study of how and how much frontier AI uplifts an expert biologist in a complex wet-lab setting.
• Maintain electronic lab notebook documentation and uphold rigorous QA/QC.
• Help colleagues across the laboratory use AI tools more effectively in their work.
• Partner closely with AI, computational, and laboratory science leadership, receiving scientific direction and computational support.

Benefits

• Comprehensive healthcare coverage, including health, dental, and vision insurance.
• Yearly mental health allowance for every employee.
• Flexible work hours.
• Unlimited paid time off.
• Minimum 12 weeks of fully paid parental leave.
• Fully paid conference sponsorships.
• Yearly professional development allowance for every employee.
• Employer 401(k) match up to total salary.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree