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Entry Level Protein Engineering Scientist information

What does an entry level protein engineering scientist do?

An Entry Level Protein Engineering Scientist assists in the design, modification, and analysis of proteins to improve their properties for specific applications, such as drug development or industrial processes. They typically work in laboratories, conducting experiments, analyzing data, and collaborating with senior scientists to optimize protein function. Responsibilities may include molecular cloning, protein expression and purification, and using computational tools for protein modeling. This role often serves as a starting point for a career in biotechnology or pharmaceutical research, providing hands-on experience with advanced laboratory techniques and scientific problem-solving.

What are the key skills and qualifications needed to thrive as an entry level protein engineering scientist?

To thrive as an Entry Level Protein Engineering Scientist, you need a solid background in molecular biology, biochemistry, and protein engineering, typically supported by a relevant bachelor’s or master’s degree. Proficiency in laboratory techniques such as PCR, mutagenesis, chromatography, and familiarity with bioinformatics tools and software like PyMOL or BLAST is important. Attention to detail, strong analytical thinking, and effective teamwork enable you to excel in experimental design and data interpretation. These skills are crucial for advancing research goals, ensuring accurate results, and contributing to collaborative scientific projects.

What are some typical challenges faced by entry level protein engineering scientists when starting in the field?

Entry-level protein engineering scientists often encounter challenges such as adapting to complex laboratory protocols, mastering advanced analytical techniques, and interpreting experimental data accurately. Collaborating effectively within multidisciplinary teams and efficiently documenting research findings are also common hurdles. However, these challenges present valuable learning opportunities and, with mentorship and regular feedback, new scientists quickly build confidence and expertise.

What are the most commonly searched types of Protein Engineering Scientist jobs in California?

The most popular types of Protein Engineering Scientist jobs in California are:

What are popular job titles related to Entry Level Protein Engineering Scientist jobs in California?

For Entry Level Protein Engineering Scientist jobs in California, the most frequently searched job titles are:

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The top searched job categories for Entry Level Protein Engineering Scientist jobs in California are:

What cities in California are hiring for Entry Level Protein Engineering Scientist jobs?

Cities in California with the most Entry Level Protein Engineering Scientist job openings:

Entry Level Protein Engineering Scientist jobs near you
Infographic showing various Entry Level Protein Engineering Scientist job openings in California as of August 2026, with employment types broken down into 84% Full Time, 10% Part Time, 5% Contract, and 1% Nights. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Co-Op, ML Scientist for Protein Engineering

Lila Sciences

San Francisco, CA • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Key responsibilities

  • Contribute to ML research projects focused on protein engineering, antibody design, and related biomolecule design problems.

  • Explore generative and predictive modeling approaches for protein sequence, structure, function, and developability.

  • Analyze biological and experimental datasets to identify patterns, evaluate model outputs, and guide design decisions.

Job description

Your Impact at LILA

Lila is embarking on a transformative mission to redefine the future of medicine by combining automated large-scale data generation with scientific superintelligence. At Lila, we don't just use AI to analyze biology; we are building the loop where AI and automation co-evolve to solve the hardest problems in medicine.

To this end, the Life Science AI team is developing machine learning systems that can reason over biological data and help design better biomolecules. We are seeking an ML Scientist Co-Op to contribute to protein engineering research, including problems related to generative protein design, antibody engineering, developability, and wet-lab-informed model iteration.

This is an opportunity to work alongside Lila scientists on applied ML research at the interface of AI and biology. You will help explore models, datasets, and workflows that connect computational protein design ideas to real experimental needs, gaining hands-on experience in a fast-moving scientific environment.

What You'll Be Building

  • Contribute to ML research projects focused on protein engineering, antibody design, and related biomolecule design problems.
  • Explore generative and predictive modeling approaches for protein sequence, structure, function, and developability.
  • Work with scientists and ML researchers to translate biological design goals into tractable computational problems.
  • Analyze biological and experimental datasets to identify patterns, evaluate model outputs, and guide design decisions.
  • Prototype workflows that connect model predictions, candidate prioritization, and wet-lab feedback.
  • Communicate results clearly through code, notebooks, written summaries, and presentations to scientific and technical collaborators.

What You'll Need to Succeed

  • Currently enrolled as a PhD student in Computer Science, Machine Learning, Computational Biology, Bioengineering, Biophysics, or a related quantitative field.
  • Research experience in machine learning, computational biology, protein engineering, or a closely related area.
  • Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.
  • Ability to work with biological sequence, structure, assay, or other scientific datasets.
  • Interest in applying ML methods to real biological design problems in partnership with experimental scientists.
  • Clear communication skills and comfort working in a collaborative, cross-disciplinary research environment.

Bonus Points For

  • Experience with protein language models, structure prediction, generative protein design, diffusion or flow-based models, or antibody design.
  • Familiarity with protein structure, biophysics, developability, affinity maturation, or wet-lab validation concepts.
  • Publications, preprints, open-source work, or research projects in ML for biology, protein engineering, or AI for Science.
  • Experience building active learning, model evaluation, or data analysis workflows for scientific discovery.
  • Comfort collaborating with experimental scientists and translating between ML concepts and biological constraints.

Apply