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Entry Level Protein Engineering Scientist Jobs in California

Arcadia Science

Computational Biologist

Emeryville, CA · On-site

$150K - $200K/yr

You'll work closely with the platform scientists and engineering team, quickly and iteratively ... Our most urgent need right now is in protein engineering and design, but we're also looking for ...

BigHat

Scientist I, Display Technologies

San Mateo, CA · On-site

$125 - $150/hr

Ph.D. in Biochemistry, Structural Biology, Protein Engineering, or a related field, with up to 2 ... Scientific track record and publication record. * Strong data analysis and interpretation skills.

Arcadia Science

Protein Designer

Emeryville, CA · On-site

$130K - $180K/yr

A Bit About Us We are Arcadia Science, an evolutionary biology company founded and led by ... Experience in at least one programming language and experience with relevant libraries * High level ...

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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:

What job categories do people searching Entry Level Protein Engineering Scientist jobs in California look for?

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.

ML Scientist I / II, Foundation Models for Life Sciences

Lila Sciences

San Francisco, CA • On-site

$176K - $304K/yr

Full-time

Medical, Dental, Vision, Life

Re-posted 11 days ago

Job description

Your Impact at Lila
Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Science AI (LSAI), the Foundation Models team builds foundation models that learn across biological sequence, molecular structure, and experimental data to power automated scientific discovery across Lila's life science domains.
We are seeking a Scientist I or II to work on structure prediction and co-folding. The team's current emphasis is protein-protein and complex prediction in support of antibody and biologics design, and on making those predictions good enough to drive real experimental decisions. You will contribute across problem formulation, model design, training, evaluation, and integration into Lila's closed-loop discovery engine.
This is an IC role for someone building deep expertise in structure-aware generative AI for biology. You will own research sub-problems end to end, collaborate closely with experimental scientists to close the computational-experimental loop, and contribute to Lila's presence in the broader scientific community.
What You'll Be Building
  • Train and evaluate structure prediction and co-folding models for protein complexes, protein-protein interactions, and related biomolecular systems
  • Build and extend models informed by AlphaFold-style co-folding, diffusion models, protein language models, and related structure-aware ML methods
  • Build rigorous evaluation frameworks to ensure model generalization to challenging de novo design problems
  • Scale training, inference, and evaluation workflows across large GPU clusters
  • Be part of the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: shape data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance
  • Contribute to adjacent foundation model research where it strengthens the structural work, including biological sequence design and multimodal scientific reasoning
  • Translate biological questions into well-defined ML problems and interpret model outputs alongside wet-lab scientists, structural biologists, and computational biologists
  • Support research quality and methodology standards within the foundation models program

What You'll Need to Succeed
  • PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or a related quantitative field (or Master's with equivalent research experience)
  • Hands-on experience training deep learning models on molecular, protein, or structural data
  • Strong foundation in generative model architectures and training, with demonstrated ability to design careful experiments, ablations, and evaluations
  • Ability to formulate and execute research independently, from problem definition through experimentation
  • Familiarity with at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or related)
  • Experience collaborating with experimental scientists or working with biological/chemical data
  • Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with GPU-based training workflows

Bonus Points For
  • Experience training or extending co-folding, structure prediction, protein-protein, or diffusion deep learning models
  • Experience with AlphaFold or AlphaFold-derived methods (e.g., Boltz, Protenix), RFdiffusion, or protein language models
  • Antibody, biologics, or protein design experience, including structure-guided optimization
  • Familiarity with distributed training infrastructure and large-scale scientific data pipelines
  • Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
  • Experience with active learning loops or closed-loop experimental workflows
  • Experience integrating ML models into agentic scientific workflows
  • High-impact publications or open-source contributions in AI for Science in relevant venues (NeurIPS, ICML, ICLR, AAAI, Nature Methods, Nature Biotechnology, or equivalent)

Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
Expected Base Salary Range
$176,000-$304,000 USD
About LILA
Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.
Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.
We're All In
Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.

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