Wet-lab Scientist – Research Study, All Levels, Multiple Openings

Wet-lab Scientist – Research Study, All Levels, Multiple Openings (10 hours total)

Location : On-site, Cambridge MA (Kendall Square); no flexibility for remote or hybrid work
Hours : Part-time, one week commitment, ~15h total hours in one week
Temporary : Fixed 1 week term (research study)
Start Dates: Cohort start dates 10/12, 10/19, 10/26, 11/2
Rate: $35-150/h (pay depends on experience/expertise)
Level: All Levels

About the Study:

- Transfyr is recruiting for a research study.
- This is a research study being conducted by Transfyr and an external sponsor that will be disclosed under NDA if selected.
- The project is seeking to measure the difficulty of different wet-lab tasks for operators with different skill levels to provide foundations for future studies of AI augmentations of laboratory tasks.
- You will be tasked with performing a Molecular Cloning protocol. Instructions will be provided.
- This is anticipated to take 10-20 hours depending on your level of expertise.
- You will be compensated at a rate per hour, to be determined based on your level of expertise.
- Your participation is voluntary and you can stop at any time.
- There are no anticipated risks of your participation, nor any anticipated benefits to you besides the compensation.

Requirements:

- Located within commuting distance from Cambridge, MA at the time of performance.
- Ability to complete the experiment in 1 week on a defined schedule:

Select a cohort: Monday & Wednesday or Tuesday & Thursday commitment
- Select a shift: morning (8a-12p), afternoon (1p-5p), or evening shift (6p-11p)
- Each day is expected to require 5 hours total.

- Relevant experience to determine level of expertise: level of experience designing, troubleshooting, and executing complex protocols related to molecular cloning, site directed mutagenesis, nucleic acid extraction, PCR, RT-qPCR, gene manipulation, transformation and transfection techniques, cell culture, and sequencing.

Scientist, Domain Expert – 5+ years specialized professional experience designing, executing, analyzing, and troubleshooting protocols relevant to genetic engineering. Includes PhD candidates and postdocs specialized in molecular cloning and genetic engineering assays.
- Scientist, Non-domain Expert – 2+ years professional lab experience and proficient in general bench work Includes 2nd year PhD or MS students with demonstrated wet-lab expertise.
- Scientist, Novice – Individuals with no professional work experience in a wet-lab setting. Includes undergraduate and MS students with experience that does not exceed foundational academic coursework

About Transfyr

Transfyr is building physical AI for science.

Why is it that a professional athlete has dramatically more information about every play they make than a scientist has about the cause of any experimental failure? At Transfyr, we are building the infrastructure to make real-world scientific work legible, transferable, and reproducible.

Modern science is capable of extraordinary outcomes, but much of the most important insights never become explicit: how experiments are actually executed, protocols drift, how experts make gametime decisions on the fly, why experiments fail on Tuesdays. This tacit knowledge is rarely captured, making it difficult to reliably reproduce results, much less hand off protocols to new team members or collaborators. We believe our systematic failure to capture tacit knowledge is holding back the entire industry.

We’re building systems that operate directly in real laboratory environments to elucidate, capture, and interpret this missing information. Our platform records and analyzes multimodal data about how scientific work is performed and turns it into durable, operational knowledge. In doing so, we are also building the world’s largest commercial dataset on real-world scientific execution.

This foundation is critical not only for driving elite human performance today, but for enabling meaningful automation tomorrow. Physical AI systems cannot learn from outcomes alone; they require rich, grounded records of how work is actually done in the real world.

Want to learn more? You can read some of our writings here .