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Remote Scientist Antibody Engineering Jobs (NOW HIRING)

Dana-Farber Cancer Institute

$38.25 - $48/hr

... as engineering hits to generate potential therapeutics, e.g. bispecific and CAR T-cell ... Data Science dept. required to run these tools. • Develop antibody selection strategies to ...

Radiance Technologies

The individual will support advanced remote sensing exploitation functions through detailed ... The position requires working collaboratively with other engineers, scientists, and analysts on ...

Riverside Research

Remote Sensing Scientist

Dayton, OH · Remote

$91K - $140K/yr

Position Overview Riverside Research is seeking a full-time Remote Sensing Scientist in support of ... Bachelor's degree in Electrical Engineering, Physics, or comparable technical degree; non-technical ...

Jade Biosciences

Jade's pipeline also includes JADE201, an afucosylated anti-BAFF-R monoclonal antibody, as well as ... The role is fully remote and will require periodic travel for investigator meetings, study team ...

Argenx

Director of Medical Writing

Our unique antibody engineering technologies combined with the complementary expertise of our ... Bachelor's degree in a scientific or clinical discipline or related field required; Ph.D. preferred

Dana-Farber Cancer Institute

... the engineering of hits to generate potential therapeutic antibody-based biologics and cell ... We dedicate time to meet trainees' individual needs for scientific growth and pursuit of their ...

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Remote Scientist Antibody Engineering information

What are the key skills and qualifications needed to thrive as a remote scientist in antibody engineering?

To thrive as a Remote Scientist in Antibody Engineering, you need a strong background in molecular biology, protein engineering, and immunology, usually supported by a PhD or relevant research experience. Familiarity with laboratory automation software, bioinformatics tools, and antibody design platforms is essential, along with experience in data analysis systems. Excellent problem-solving skills, effective communication, and the ability to collaborate virtually are key soft skills for this remote role. These competencies ensure successful design, analysis, and optimization of antibodies, driving innovation and productivity in a distributed research environment.

What is the difference between Remote Scientist Antibody Engineering vs Remote Scientist Immunology?

AspectRemote Scientist Antibody EngineeringRemote Scientist Immunology
Required CredentialsPhD in Immunology, Biochemistry, or related field; experience in antibody design and engineeringPhD in Immunology, Microbiology, or related field; focus on immune system mechanisms
Work EnvironmentLaboratory research, computational modeling, biotech companiesLaboratory research, clinical settings, biotech and pharma industries
Employer & Industry UsageBiotech firms, pharmaceutical companies, research institutesResearch institutions, biotech, pharma, healthcare organizations

Remote Scientist Antibody Engineering focuses on designing and developing therapeutic antibodies, often involving protein engineering and molecular biology techniques. In contrast, Remote Scientist Immunology studies immune system functions and mechanisms, which may include antibody responses but also broader immune processes. Both roles require advanced degrees and are common in biotech and pharma industries, but they differ in their specific focus and daily tasks.

What does a remote scientist in antibody engineering do?

A Remote Scientist in Antibody Engineering works on the design, development, and optimization of antibodies for research, diagnostics, or therapeutic use, often from a location outside of a traditional laboratory. Their responsibilities may include analyzing data, performing computational modeling, collaborating with cross-functional teams, and using bioinformatics tools to predict antibody structures and functions. Remote scientists leverage digital communication and cloud-based platforms to collaborate effectively and contribute to scientific advancements without needing to be on-site. This role requires a strong background in molecular biology, immunology, and protein engineering, as well as proficiency with relevant software and databases.

How does a remote scientist in antibody engineering typically collaborate with cross-functional teams?

As a Remote Scientist specializing in Antibody Engineering, collaboration with cross-functional teams—such as bioinformatics, protein production, and clinical research—is essential. Communication is usually managed through virtual meetings, shared digital platforms, and collaborative documentation tools to ensure seamless progress on projects. While working remotely, you’ll often participate in data reviews, experimental design sessions, and troubleshooting discussions, making strong written and verbal communication skills crucial. Regular updates and clear reporting structures help maintain alignment and project momentum, even when team members are in different locations.
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Remote Scientist Antibody Engineering jobs near you
Infographic showing various Remote Scientist Antibody Engineering job openings in the United States as of August 2026, with employment types broken down into 90% Full Time, 5% Part Time, and 5% Contract. Highlights an 100% Remote job distribution.

Scientist II, ML - Guided Protein Design Evaluation

Profluent

Emeryville, CA • On-site, Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 13 days ago

Job description

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.
We are expanding the ML Design Evaluation (MDE) program; the cross-functional program that runs dedicated, structured design campaigns to generate high-quality experimental data for Profluent's protein design models. MDE sits at the intersection of ML, Biology, and Bioinformatics, and its output directly translates into improved models and accelerated protein design campaigns.
We are looking for a scientist with deep expertise in therapeutic antibody development, protein engineering, and ML-guided protein design to partner with the MDE Lead on the scientific substance of the program. You will own the translation between what our ML team needs to learn from each campaign and what the experimental system (internal assays and CRO-run workflows) can deliver. You will help scope targets and assays, partner with ML scientists to define what "model-ready" data looks like for each campaign, set and enforce assay readiness and QC expectations, and close the Design→ Build→ Test→ Learn loop by tracking how each dataset impacts model performance.
This is a hands-on technical role, not a pure program role. You'll collaborate with ML and Protein Design scientists to develop experimental designs, review assay protocols, troubleshoot data quality issues with internal teams and CROs, and work closely with ML scientists to understand how data is used in model training.
Responsibilities
  • Partner with ML and Protein Design leads to scope MDE campaigns; including target selection, choice of assays, and what readouts are needed to improve the next generation of models
  • Define assay readiness levels, controls, and QC acceptance criteria for each campaign, and enforce them across internal execution and external CROs
  • Review incoming data (biochemical, biophysical, NGS-based screens) for scientific soundness before it is accepted into the data warehouse and used for model training
  • Work with Bioinformatics on metadata schemas and data ingestion so that every dataset is consistent, queryable, and traceable back to its parent project.
  • Serve as the scientific point of contact for CRO technical scoping; evaluate whether a vendor's platform, assay conditions, and controls are fit for purpose for ML-guided optimization
  • Contribute to campaign charters, benchmarking assay design, and post-campaign readouts; help turn individual experiments into a growing institutional dataset
  • Represent the MDE program in cross-functional technical forums and help raise the data-quality bar across the organization

Qualifications
  • PhD in Molecular Biology, Biochemistry, Protein Engineering, Biophysics, Immunology, or a closely related field; or MS with equivalent industry experience
  • 5+ years of hands-on experience with protein engineering and functional characterization, including biochemical activity, binding, stability, and/or expression assays
  • Direct experience with therapeutic antibody discovery and engineering (e.g., affinity maturation, developability optimization, humanization, format engineering) and the assays that support it (binding kinetics, epitope characterization, biophysical and developability panels)
  • Strong working knowledge of NGS-based screening workflows (e.g., deep mutational scanning, amplicon sequencing, high-throughput activity screens, antibody display library sequencing) and what makes that data usable for modeling
  • Demonstrated ability to define assay quality standards (signal/noise, reproducibility, plate-level controls) and hold experimental workflows to them
  • Fluent working across scientific disciplines; can talk protein chemistry and antibody biology with biologists and model-guided design approaches with ML scientists without losing either audience

Preferences
  • Prior experience closing Design-Build-Test-Learn loops in an industrial protein design, antibody engineering, or directed evolution setting
  • Experience with display-based antibody discovery platforms (phage, yeast, mammalian) and/or single-cell B-cell screening workflows
  • Experience scoping and overseeing externally run assays at CROs, including technical evaluation of vendor platforms
  • Comfortable with Python/pandas and SQL at the level needed to inspect, QC, and reason about experimental datasets independently
  • Familiarity with LIMS and project-management systems and experience defining data & metadata schemas for experimental data
  • Exposure to kinases, nucleases, recombinases, or gene editing enzymes is a plus
  • Track record of publishing, presenting, or shipping work at the intersection of protein engineering and machine learning

What We Offer
  • High-growth opportunity with meaningful impact on the future of protein design
  • Competitive compensation package with equity participation
  • 401(k) with a strong employer match
  • Comprehensive benefits including health/dental/vision insurance
  • Generous PTO policy and commitment to work-life balance
  • Professional development opportunities in a cutting-edge field at the intersection of AI and biology

Profluent Bio, Inc is an equal opportunity employer promoting diversity and inclusion in the workspace. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical conditions, veteran status, sexual orientation, gender (including gender identity and gender expression), sex (which includes pregnancy, childbirth, and breastfeeding), genetic information, taking or requesting statutorily protected leave, or any other basis protected by law.
Work Authorization Requirement
Applicants must have ongoing work authorization in the United States that does not require employer sponsorship. Sponsorship will not be provided now or at any time in the future for this position.
Employment Eligibility Verification
Legal authorization to work in the United States is required. In compliance with federal law, all persons hired must verify their identity and work eligibility and complete the required employment verification form upon hire.
Hiring Salary Range
$147,000-$180,000 USD

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