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Computational Protein Design

We are seeking a Computational Protein Design Scientist to join our team working at the interface of generative AI and synthetic biology. You will play a key role amongst a team of scientists designing and engineering proteins for specific functions. This is an opportunity to help shape and grow an organization that advances artificial intelligence and applies it to longstanding scientific challenges. Using your blend of computational expertise and in-depth biochemical understanding of proteins, you will generate insights to improve protein functionality and operate at the interface between our machine learning and experimental platform units, working closely to seamlessly integrate AI generations and lab validation data.

Who you are

  • You are a computational protein designer. You have a proven track record of successfully leveraging novel computational tools and knowledge of biochemistry or structural biology to design proteins to functional requirements and applications in synthetic biology.

  • You are a data scientist. You are a strong data scientist and you have experience owning data-driven projects to generate biological insights.

  • You are a successful scientist. You have a PhD (or equivalent industry experience) in computational biology, bioinformatics, computer science, biochemistry, structural biology, physics, biophysics, bio/chem engineering, or a related field. Your research experience was in protein biochemistry using computational expertise.

  • You collaborate with experimentalists. You have experience collaborating with experimental (i.e. wet lab) teams to achieve protein design objectives.

  • You are an owner. You have a proven track record of delivering successful commercial and / or academic research projects, demonstrated through publications, patents, and/or commercially impactful outcomes, as well as other contributions to the scientific community.

  • You are a connector. You love to connect people and enable them to perform at their highest levels. You have excellent communication and presentation skills with the ability to convey complex scientific concepts to both technical and non-technical audiences.

  • You are a mission driven innovator. You are passionate about making a positive impact on the world, whether it's for patients, partners or beyond. You are motivated by the end goal and are flexible in adapting to different approaches and methodologies.

  • You thrive in a dynamic and ambiguous environment. You excel in a fast-paced setting where goals must be achieved efficiently and urgently. You have a keen eye for creating, then optimizing processes to improve speed and repeatability. You are an advocate for lab automation, both through hardware and software

What sets you apart (preferred but not required)

  • You have experience with generative AI. You have experience leveraging generative AI (or other machine learning models) in synthetic biology applications.

  • You have experience engineering gene editing tools, such as nucleases and integrases.

  • You have experience with homology-based and structural bioinformatics, and are able to answer scientific questions using very large databases.

  • You have helped scale a young biotech before. You have worked in startups and helped the company grow.

Your Responsibilities

  • Leverage our proprietary generative AI models to design proteins for experimental validation:

    • Analyze protein design problems based on functional requirements, biochemistry, structural biology and sequence homology

    • Generate designs using our proprietary generative AI models and optimize designs for experimental validation

    • Coordinate with our lab-based protein engineers to plan and optimize the design process and validation strategy

  • Leverage our proprietary data to improve our models:

    • Analyze and leverage our experimental results to improve the next round of designs and increase our success rate over validation rounds

    • Collaborate with machine learning scientists to fine-tune and prompt our models

  • Collaboration and communication:

    • Be an effective interface between machine learning model development and experimental validation

    • Capture bioengineering learnings and feedback to our machine learning unit, and vice versa

    • Foster a collaborative and innovative environment, proactively finding opportunities to innovate and create clarity and alignment between different units

  • Contribute to our computational tools:

    • Help improve the way we use, serve and integrate our AI models, by feeding back to the software engineers and foundational machine learning unit

    • Help improve our data management systems and workflows

  • Scientific excellence and self development:

    • Work to the highest scientific standards (publication-grade work)
      Stay on top of relevant developments in synthetic biology

    • Continue building your understanding of generative AI as well as expanded areas of protein and cell biology

    • Participate in knowledge sharing, e.g. organize and present at our internal reading group.

    • Attend and present at conferences when relevant.

Apply

We offer strongly competitive compensation and benefits packages, including:

  • Private health insurance

  • Pension/401(K) contributions

  • Generous leave policies (including gender neutral parental leave)

  • Hybrid working

  • Travel opportunities and more

We also offer a stimulating work environment, and the opportunity to shape the future of synthetic biology through the application of breakthrough generative models.

We welcome applicants from all backgrounds and we are committed to building a team that represents a variety of backgrounds, perspectives, and skills.

Average salary estimate

$115000 / YEARLY (est.)
min
max
$90000K
$140000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Computational Protein Design, Latent Labs

Join our innovative team as a Computational Protein Design Scientist in San Francisco! Here, you'll merge the exciting worlds of generative AI and synthetic biology, working hand-in-hand with fellow scientists to design proteins tailored for specific functions. This is your chance to make an impact in a cutting-edge organization that's using AI to tackle longstanding scientific challenges. With your strong computational skills and deep biochemical knowledge, you'll contribute to improving protein functionality while expertly integrating AI-generated designs with lab validation data. We’re looking for someone with a solid history of leveraging computational tools along with a PhD or equivalent experience in computational biology, bioinformatics, or related fields. You’ll collaborate with hands-on experimental teams and take ownership of data-driven projects that unveil new biological insights. If you thrive in dynamic environments, have a passion for innovation, and want to make a difference, this role is a perfect fit for you! We offer competitive compensation, benefits, and a stimulating work culture that encourages growth and collaboration. Grab this exciting opportunity to be at the forefront of synthetic biology with us!

Frequently Asked Questions (FAQs) for Computational Protein Design Role at Latent Labs
What are the responsibilities of a Computational Protein Design Scientist at this company?

As a Computational Protein Design Scientist, your responsibilities will include leveraging proprietary generative AI models to design proteins for experimental validation. You will analyze protein design issues based on functional requirements and collaborate closely with lab-based engineers to optimize design processes. Your role will also involve improving these models using experimental results and fostering collaboration between machine learning and experimental teams.

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What qualifications do I need to apply for the Computational Protein Design Scientist position?

To apply for the Computational Protein Design Scientist position, candidates should have a PhD or equivalent experience in computational biology, bioinformatics, biochemistry, or a closely related field. A track record of successfully designing proteins through computational methods, as well as the ability to collaborate with experimental teams, is crucial. Additional experience with generative AI or gene editing tools is preferred but not required.

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What skills are essential for success as a Computational Protein Design Scientist at this organization?

Essential skills for a Computational Protein Design Scientist include proficiency in computational tools and data science, a strong foundation in biochemistry and structural biology, and excellent communication abilities. You should be able to convey complex scientific concepts to diverse audiences, collaborate effectively with experimental teams, and thrive in fast-paced environments where you can innovate and drive projects forward.

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How does the company support professional development for Computational Protein Design Scientists?

The company actively supports professional growth through opportunities for knowledge sharing, participation at conferences, and staying up to date on relevant developments in synthetic biology. As a Computational Protein Design Scientist, you are encouraged to contribute to internal discussions, present findings, and engage in ongoing learning about generative AI and protein biology, ensuring you stay at the forefront of your field.

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What kind of work environment can I expect as a Computational Protein Design Scientist?

You can expect a stimulating and collaborative work environment that emphasizes innovation, flexibility, and scientific rigor. Our culture encourages diverse perspectives, making it the ideal place for a Computational Protein Design Scientist to make a significant impact in the field of synthetic biology with cutting-edge technologies and processes.

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Common Interview Questions for Computational Protein Design
Can you describe your experience with computational tools in protein design?

Focus on specific projects where you utilized computational methods to design proteins that meet functional requirements. Highlight any tools or software you used, the challenges faced during the process, and the outcomes of your designs, linking them back to real-world applications.

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What strategies do you use for collaborating with experimental teams?

Emphasize the importance of open communication and mutual understanding of goals. Describe your approach to integrating computational designs with lab validation, including how you adapt your working style to complement the experimentalists' methodologies.

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How do you ensure the accuracy of your protein designs?

Discuss the importance of validation and cross-referencing data from different sources, using statistical methods to analyze the accuracy of your designs. Mention any specific metrics or checkpoints that you follow to assess design effectiveness.

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Can you provide an example of a project where you utilized generative AI in protein design?

Share a detailed example outlining the problem you addressed, the generative AI models you employed, and how you translated the generated outputs into successful experimental prototypes. Discuss the collaboration process with both computational and experimental teams.

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What challenges have you faced in your projects and how did you overcome them?

Identify specific challenges you encountered, such as data discrepancies, model limitations, or communication barriers with experimental teams, and explain how you addressed them. Focus on your problem-solving skills and adaptability.

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Describe your experience with optimizing workflows or processes in a research environment.

Highlight specific workflows you improved, the methods you used for optimization, and the results achieved, such as increased efficiency or reduced processing time. Mention any tools or frameworks that facilitated these improvements.

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How do you handle feedback from colleagues in a collaborative environment?

Discuss your approach to constructive feedback, emphasizing your receptiveness to colleagues’ insights and how you incorporate that feedback into your work. Provide examples of how feedback led to better outcomes in your projects.

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What motivates you to stay updated with the latest developments in synthetic biology?

Articulate your passion for the field and the importance of staying informed about innovations and breakthroughs. Mention specific resources, journals, or conferences you follow to continually enhance your knowledge and skills.

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What do you believe are the future trends in computational protein design?

Share your thoughts on advancements in AI technologies, machine learning applications, and how these developments will shape the future of synthetic biology. Discuss potential areas for innovation and the impact they may have on research and industry.

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How do you approach the balance between creativity and scientific rigor in your designs?

Explain that innovation is essential in protein design but must always be guided by rigorous scientific principles. Talk about your process for ensuring creativity while maintaining reproducibility and scientific integrity.

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DATE POSTED
April 17, 2025

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