ML Engineer, LS AI
San Francisco, CA · On-site
... computational-experimental loop, and represent Lila's work to the broader scientific community. What You'll Be Building * Drive research on structure prediction and co-folding models for protein ...
ML Engineer, LS AI
San Francisco, CA · On-site
... computational-experimental loop, and represent Lila's work to the broader scientific community. What You'll Be Building * Drive research on structure prediction and co-folding models for protein ...
... structure determination using structural and/or sequence-based methods and/or artificial ... protein design, engineering, or prediction • Critically evaluates experimental data and ...
... structure determination using structural and/or sequence-based methods and/or artificial ... protein design, engineering, or prediction • Critically evaluates experimental data and ...
$88K - $135K/yr
Utilize Density Functional Theory (DFT) and other QM methods to calculate electronic structures and ... Translate computational predictions into actionable design parameters for the "wet lab" team and ...
$88K - $135K/yr
Utilize Density Functional Theory (DFT) and other QM methods to calculate electronic structures and ... Translate computational predictions into actionable design parameters for the "wet lab" team and ...
... structure determination using structural and/or sequence-based methods and/or artificial ... protein design, engineering, or prediction • Critically evaluates experimental data and ...
... structure determination using structural and/or sequence-based methods and/or artificial ... protein design, engineering, or prediction • Critically evaluates experimental data and ...
Computational Scientist
San Francisco, CA · On-site
About the Role We're hiring a Computational Scientist to help curate, build, and scale Tamarind ... models for structure prediction, protein design, docking, scoring, and other core biological AI ...
Computational Scientist
San Francisco, CA · On-site
About the Role We're hiring a Computational Scientist to help curate, build, and scale Tamarind ... models for structure prediction, protein design, docking, scoring, and other core biological AI ...
Perform computational analysis of protein structures and dynamics to identify target mutations for ... Preferred training in protein modeling, including structural prediction (AlphaFold, Rosetta) and ...
Perform computational analysis of protein structures and dynamics to identify target mutations for ... Preferred training in protein modeling, including structural prediction (AlphaFold, Rosetta) and ...
You will work alongside top computational protein scientists, AI/ML specialists, protein structure ... structure-prediction outputs are translated into clear experimental recommendations and fast ...
You will work alongside top computational protein scientists, AI/ML specialists, protein structure ... structure-prediction outputs are translated into clear experimental recommendations and fast ...
Computational and Experimental Scientist
San Francisco, CA · On-site
$3.0K - $5.0K/mo
About the Role This is a full-stack computational and experimental role at an early-stage AI-driven ... models, protein language models (such as ESM or ProtT5), or structure prediction systems in an ...
New
Quick apply
Computational and Experimental Scientist
San Francisco, CA · On-site
$3.0K - $5.0K/mo
About the Role This is a full-stack computational and experimental role at an early-stage AI-driven ... models, protein language models (such as ESM or ProtT5), or structure prediction systems in an ...
New
Computational and Experimental Scientist
San Francisco, CA · On-site
$3.0K - $5.0K/mo
About the Role This is a full-stack computational and experimental role at an early-stage AI-driven ... models, protein language models (such as ESM or ProtT5), or structure prediction systems in an ...
New
Computational and Experimental Scientist
San Francisco, CA · On-site
$3.0K - $5.0K/mo
About the Role This is a full-stack computational and experimental role at an early-stage AI-driven ... models, protein language models (such as ESM or ProtT5), or structure prediction systems in an ...
New
Senior Scientist - Experimental Protein Design for Target Discovery
South San Francisco, CA · On-site
$110K - $150K/yr
... structure-function relationships. * Experience and interest in collaborating with computational protein design teams and translating in silico designs into experimentally testable reagents.
Senior Scientist - Experimental Protein Design for Target Discovery
South San Francisco, CA · On-site
$110K - $150K/yr
... structure-function relationships. * Experience and interest in collaborating with computational protein design teams and translating in silico designs into experimentally testable reagents.
... protein structure alignment, binding pocket evaluation, and molecular docking * Work ... prediction * Strong technical background in machine learning, deep learning, and statistical ...
Quick apply
... protein structure alignment, binding pocket evaluation, and molecular docking * Work ... prediction * Strong technical background in machine learning, deep learning, and statistical ...
Senior Scientist - Experimental Protein Design for Target Discovery
South San Francisco, CA · On-site
$110K - $150K/yr
... structure-function relationships. * Experience and interest in collaborating with computational protein design teams and translating in silico designs into experimentally testable reagents.
Senior Scientist - Experimental Protein Design for Target Discovery
South San Francisco, CA · On-site
$110K - $150K/yr
... structure-function relationships. * Experience and interest in collaborating with computational protein design teams and translating in silico designs into experimentally testable reagents.
Senior Scientist - Experimental Protein Design for Target Discovery
South San Francisco, CA · On-site
$110K - $150K/yr
... structure-function relationships. * Experience and interest in collaborating with computational protein design teams and translating in silico designs into experimentally testable reagents.
Senior Scientist - Experimental Protein Design for Target Discovery
South San Francisco, CA · On-site
$110K - $150K/yr
... structure-function relationships. * Experience and interest in collaborating with computational protein design teams and translating in silico designs into experimentally testable reagents.
Contract Research Scientist, Computational Biology & AI/ML
Boston, MA · On-site
$38.25 - $48/hr
... prediction, and de novo protein design. * Apply AI/ML techniques to identify and rank promising ASO ... Develop well-structured, maintainable code and computational documentation that enables scientists ...
Contract Research Scientist, Computational Biology & AI/ML
Boston, MA · On-site
$38.25 - $48/hr
... prediction, and de novo protein design. * Apply AI/ML techniques to identify and rank promising ASO ... Develop well-structured, maintainable code and computational documentation that enables scientists ...
Postdoctoral Fellow, Structural Biology
Buffalo, NY · On-site
$70K - $85K/yr
... predict protein ensemble structures. As an Empire AI-funded fellow, you will have early access to ... Computational Optimization: Resolving hardware-specific performance and numerical precision ...
Postdoctoral Fellow, Structural Biology
Buffalo, NY · On-site
$70K - $85K/yr
... predict protein ensemble structures. As an Empire AI-funded fellow, you will have early access to ... Computational Optimization: Resolving hardware-specific performance and numerical precision ...
Perform computational analysis of protein structures and dynamics to identify target mutations for ... Preferred training in protein modeling, including structural prediction (AlphaFold, Rosetta) and ...
Perform computational analysis of protein structures and dynamics to identify target mutations for ... Preferred training in protein modeling, including structural prediction (AlphaFold, Rosetta) and ...
Advance the state of the art : push forward sequence-structure-function understanding with a focus ... or prediction. * Hands-on experience with machine learning and generative modeling for protein ...
Advance the state of the art : push forward sequence-structure-function understanding with a focus ... or prediction. * Hands-on experience with machine learning and generative modeling for protein ...
Perform computational analysis of protein structures and dynamics to identify target mutations for ... Preferred training in protein modeling, including structural prediction (AlphaFold, Rosetta) and ...
Perform computational analysis of protein structures and dynamics to identify target mutations for ... Preferred training in protein modeling, including structural prediction (AlphaFold, Rosetta) and ...
... computational-experimental loop, and contribute to Lila's presence in the broader scientific ... Train and evaluate structure prediction and co-folding models for protein complexes, protein ...
... computational-experimental loop, and contribute to Lila's presence in the broader scientific ... Train and evaluate structure prediction and co-folding models for protein complexes, protein ...
... stack (structure prediction, protein-ligand co-folding, affinity, and/or generative design ... Apply computational and data-analysis methods to structural/sequence datasets to guide model ...
... stack (structure prediction, protein-ligand co-folding, affinity, and/or generative design ... Apply computational and data-analysis methods to structural/sequence datasets to guide model ...
Computational Protein Structure Prediction information
What is the difference between Computational Protein Structure Prediction vs Molecular Dynamics Simulation?
| Aspect | Computational Protein Structure Prediction | Molecular Dynamics Simulation |
|---|---|---|
| Primary Focus | Predicting 3D protein structures from amino acid sequences | Simulating physical movements of molecules over time |
| Required Skills | Bioinformatics, structural biology, programming | Physics, chemistry, computational modeling |
| Work Environment | Research labs, biotech companies, academia | Research labs, pharmaceutical industry, academia |
| Common Usage | Structure prediction, drug design, functional annotation | Understanding protein dynamics, stability, interactions |
Computational Protein Structure Prediction focuses on determining the 3D structure of proteins based on their amino acid sequences, essential for understanding function and drug design. Molecular Dynamics Simulation, on the other hand, models the physical movements of molecules over time to study stability and interactions. Both roles require strong computational skills and are used extensively in research and industry, but they serve different purposes within structural biology.
What other helpful pages are available for Computational Protein Structure Prediction?
Other pages related to Computational Protein Structure Prediction:
ML Engineer, LS AI
San Francisco, CA • On-site
Lila Sciences
Full-time
Medical, Dental, Vision, Life
Re-posted 16 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 Senior or Principal ML Engineer to set the direction for our 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 own models end to end, from problem formulation and architecture through training at scale, evaluation, and integration into Lila's closed-loop discovery engine.
This is a high-impact IC role for someone operating at the frontier of structure-aware generative AI for biology. You will shape the technical agenda for structural foundation model research, collaborate closely with experimental scientists to close the computational-experimental loop, and represent Lila's work to the broader scientific community.
What You'll Be Building
What You'll Need to Succeed
Bonus Points For
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
$252,000-$374,000 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.
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 Senior or Principal ML Engineer to set the direction for our 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 own models end to end, from problem formulation and architecture through training at scale, evaluation, and integration into Lila's closed-loop discovery engine.
This is a high-impact IC role for someone operating at the frontier of structure-aware generative AI for biology. You will shape the technical agenda for structural foundation model research, collaborate closely with experimental scientists to close the computational-experimental loop, and represent Lila's work to the broader scientific community.
What You'll Be Building
- Drive research on structure prediction and co-folding models for protein complexes, protein-protein interactions, and related biomolecular systems
- Design, train, and evaluate models that advance the state of the art in AlphaFold-style co-folding, diffusion models, protein language models, and related structure-aware ML methods
- Set the evaluation bar for the program, building frameworks that establish model generalization to challenging de novo design problems
- Own training, inference, and evaluation at scale across large GPU clusters
- Shape the end-to-end ML process within Lila's "Lab-in-the-Loop" lifecycle: steer data generation strategy, build pipeline models, and design feedback loops where experimental results improve model performance
- Extend into adjacent foundation model research where it strengthens the structural work, including biological sequence design and multimodal scientific reasoning
- Translate complex biological questions into well-defined ML problems and interpret model outputs in collaboration with wet-lab scientists, structural biologists, and computational biologists
- Advance research standards and methodology within the foundation models program, contributing insights that influence approaches across adjacent teams
- Represent Lila's foundation model research externally through publications at premier venues, conference presentations, and community engagement
What You'll Need to Succeed
- PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, or a related quantitative field
- Demonstrated ability to formulate and drive research programs independently, from problem definition through publication and deployment
- Fluency across ML and at least one life science domain (structural biology, protein engineering, molecular biology, genomics, or related), with experience designing computational experiments grounded in biological reality
- Strong track record of cross-functional collaboration with experimental scientists, translating between ML and biology
- Expertise in ML frameworks (PyTorch, JAX, or TensorFlow) and experience with large-scale distributed training infrastructure (AWS, GCP, or on-prem clusters)
Bonus Points For
- Strong expertise in structure prediction, co-folding, geometric deep learning, or structure-aware molecular ML, with a track record of training these models
- Experience with AlphaFold or AlphaFold-derived methods (e.g., Boltz, Protenix), RFdiffusion, or protein language models
- Experience in computational protein design, particularly antibody and nanobody engineering
- Strong expertise in generative model architectures and training, with hands-on experience training models on distributed infrastructure
- Experience designing biological sequences or molecular structures with demonstrated wet-lab validation
- Contributions to open-source ML tools, frameworks, or benchmark datasets for scientific applications
- Experience with agentic frameworks or active learning loops in scientific contexts
- Multiple high-impact first-author or senior-author publications, or open-source contributions in AI for Science, at premier 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
$252,000-$374,000 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.