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Profluent

Scientist II, Screening

Emeryville, California, United States

Location
Emeryville, California, United States
Work
On-site
Employment
Not stated
Level
Mid level
Function
Science & research
Pay
$144K – $160K a year
Posted
3 weeks ago

Pay and the market here

The top of this role’s range, $160K a year, is about the median of $156K for mid level science jobs in California (middle half $85K – $200K, 83 jobs that state pay).

16 science jobs open in Emeryville: 0% remote, 8 posted in the last 30 days.

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About the role

Profluent is the frontier AI lab for biology. Profluent builds powerful foundation models for all of life's molecules, unlocking solutions that transform medicine, agriculture, and beyond. Founded in 2022 and headquartered in Emeryville, CA, Profluent is backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures and has raised over $150M to date.

The Role

Profluent is seeking a motivated, rigorous, and collaborative scientist to establish and lead a centralized gene editor screening function. High-quality experimental data is a core input to our protein design models, and this role will own the screening operations that produce it.

This role centers on building and running a screening core responsible for assay standardization, data quality, and team development. At first, you will support campaigns that generate training and evaluation data for our protein design models, and the scope may grow to cover more programs. You will design, run, and troubleshoot mammalian genome editing assays with NGS readouts in arrayed and pooled formats. You will set the technical standards that make our data reproducible and trustworthy. You will also build and mentor a team of Research Associates, and coordinate closely with Automation, Bioinformatics, Data Science, and Biology to keep the screening pipeline running smoothly from start to finish.

As a leader of the team, you will balance hands-on scientific execution with people management and process development. You will identify, prioritize, and drive infrastructure projects that improve throughput, consistency, and assay sensitivity. You will also work with leadership to shape the long-term scope of the core.

We welcome candidates from diverse backgrounds who share our commitment to revolutionizing biology, driving meaningful innovation, and making a lasting impact on global healthcare.

This role offers a unique opportunity to shape the future of AI-driven protein design while collaborating with a dynamic, interdisciplinary team of experts in machine learning, protein engineering, cell biology, and gene editing.

Responsibilities

Team Leadership

  • Build and lead a team of 2–4 Research Associates, including hiring, training, performance management, and career development
  • Train and mentor staff on aseptic technique, assay execution, and documentation standards
  • Foster a supportive team culture that prioritizes growth, engagement, and well-being

Execution

  • Prioritize, schedule, and resource cross-functional experiments in coordination with Automation and Bioinformatics
  • Design, execute, troubleshoot, and analyze mammalian genome editing assays with NGS readouts in arrayed and pooled formats
  • Onboard new assays and extend existing assays to new enzyme classes
  • Maintain sample chain of custody from plate mapping through automation, sample preparation, and sequencing submission, partnering with Bioinformatics on data schema and capture
  • Communicate technical results clearly to both expert and non-expert audiences
  • Deliver QC'd, analysis-ready datasets with documented QC flags to ML

Process Development

  • Define and enforce technical standards, and write and maintain SOPs
  • Qualify new assays and define acceptance criteria based on reproducibility data
  • Maintain cell line quality and inventory for screening assays, in coordination with teams that manage cell banks
  • Characterize assay sensitivity, including the minimum detectable difference in activity at standard replicate counts
  • Identify, prioritize, and plan infrastructure projects that continually improve assays and processes
  • Coordinate process development across Automation, Bioinformatics, Data Science, and Biology
  • Evaluate the costs and benefits of centralizing high-throughput workflows in partnership with other teams
  • Work with leadership to assess and refine the scope of core functions

Qualifications

  • PhD in Molecular Biology, Biochemistry, Cell Biology, or a related field and 2+ years of relevant industry experience, or MS in a related field and 10+ years of relevant industry experience
  • Hands-on experience with bacterial and mammalian cell culture, genome editing, and NGS-based assays
  • Experience with high-throughput experimentation and analysis of large datasets
  • Experience with LIMS or ELN systems for sample and experiment tracking
  • Demonstrated ability to establish technical standards and drive consistent execution across operators

Preferences

  • Experience working in or building a core facility or centralized service function
  • Track record of building or improving high-throughput processes
  • Experience leading infrastructure or process improvement projects
  • Experience with liquid handling automation and tech transfer
  • Experience with cell bank and inventory management
  • Strong foundation in experimental design and statistics, including power analysis and design of experiments (DOE)

Hiring Salary Range

$144,000—$160,000 USD

NOTICE TO STAFFING AND SEARCH FIRMS: Profluent does not accept unsolicited candidate submissions. We ask that agencies refrain from sending resumes to our careers inbox, hiring managers, or any Profluent employee. Any resume submitted without a signed agreement and an active search request from our Talent team will be treated as the property of Profluent, and no placement fee will be owed.

Source

Posted by Profluent on their own hiring page, job-boards.greenhouse.io. OptiTalent shows it as published, read 07 Nov 2026 11:49 UTC; the original is the reference.

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