The posting
Location: On-site, Cambridge MA (Kendall Square); no flexibility for remote or hybrid work
Hours: Part-time, one week commitment, ~3 consecutive days, ~24-30 hours total working time Temporary: Fixed 1 week term (research study) Start Dates: Cohort start dates 11/17, 11/18, 11/23, 11/30
Rate: $35-75/h (pay depends on experience/expertise) Performance Bonus: $0 - $10,000, see below
Level: Non-domain expert scientists (between 2-5 years of hands-on wet-lab experience), limited experience with bacterial cloning Exclusion criteria: You may not participate if you have already participated in a Transfyr research study in 2026
About the Study:
- Transfyr is recruiting for a research study.
- The project is seeking to measure AI’s ability to uplift scientists in a variety of wet-lab tasks.
- You will be tasked with designing and executing an experiment to achieve an experimental result.
- You will be assigned to one of three arms in the study: internet access only, AI model 1 access only, AI model 2 access only.
- The experiment is anticipated to take ~24-30 hours total working time spanning 3-4 consecutive days, 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.
- You may be asked to take an AI proficiency test prior to being invited to participate.
- A performance bonus will be awarded for successful completion (see below).
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 start date: 11/17, 11/18, 11/23, 11/30 Select available working hours: morning (8a-12p), afternoon (1p-5p), or evening shift (6p-11p) Each day is expected to require 4-8 hours.
- Scientist, Non-domain Expert. 2-5 years professional lab experience and proficient in general bench work Includes 2nd year PhD or MS students with demonstrated wet-lab expertise. We are prioritizing individuals with limited experience with bacterial cloning.
- Exclusion criteria: You may not participate if you have participated in a Transfyr research study between June - October 2026.
Performance bonus for time to validated sample:
- The performance-based bonus will reward successful completion and faster time from the first pipetting action to a validated sample within the existing approved experimental scope.
- Half of the $10,000 pool ($5,000) will be divided equally among successful operators.
- The remaining $5,000 will be distributed in proportion to each successful operator’s inverse adjusted completion time, so faster successful operators receive larger awards.
- The minimum performance bonus is $0, if an operator is unsuccessful.
- The maximum performance bonus is $10,000 if only one operator in the study is successful.
- If all participants (n=15) are equally successful, each operator will receive a performance bonus of $667.
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.
