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Ai For Science Jobs (NOW HIRING)

UAB

SCIENTIST III

Tampa, FL · On-site

The role requires deep technical expertise across AI for science protein and antibody design, AI-driven molecular dynamics, agentic AI and autonomous research systems, clinical trial simulations ...

Meta

AI Research Engineer, FAIR Chemistry

Menlo Park, CA · On-site

$121K/yr

Provide technical leadership for a scientific direction in AI for science. * Perform research that advances the state-of-the-art in AI for chemistry and materials science. * Work closely with ...

UAB NOVA RENT

SCIENTIST III

Birmingham, AL · On-site

The role requires deep technical expertise across AI for science protein and antibody design, AI-driven molecular dynamics, agentic AI and autonomous research systems, clinical trial simulations ...

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Ai For Science information

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How much do ai for science jobs pay per year?

As of Jul 9, 2026, the average yearly pay for ai for science in the United States is $48,391.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,500.00 and $52,000.00 per year, depending on experience, location, and employer.

How does collaboration typically work between AI for Science professionals and domain experts in research teams?

AI for Science professionals frequently work closely with experts in fields such as biology, chemistry, or physics to identify scientific problems that can benefit from machine learning techniques. Collaboration usually involves regular meetings to translate complex scientific challenges into data-driven models, sharing domain knowledge, and iteratively refining solutions. Effective communication and a willingness to bridge gaps between computational and scientific perspectives are essential. This interdisciplinary teamwork not only enhances the impact of AI solutions but also fosters ongoing learning and innovation.

Which 3 jobs will survive AI?

For roles related to AI for science, jobs such as research scientists, data analysts, and laboratory technicians are likely to persist as they require specialized knowledge, critical thinking, and hands-on experimentation that AI cannot fully replicate. These positions often involve complex problem-solving, interpretation of experimental data, and domain-specific expertise. Continuous learning and proficiency with AI tools can enhance job security in these fields.

What is AI for Science?

AI for Science refers to the application of artificial intelligence and machine learning techniques to accelerate scientific discovery and research. By leveraging large datasets, complex models, and advanced computational methods, AI helps scientists analyze data, identify patterns, simulate experiments, and make predictions across various scientific fields such as biology, chemistry, physics, and climate science. This approach can significantly speed up research, uncover new insights, and solve problems that were previously too complex or time-consuming for traditional methods.

What AI can I use for science?

AI for science involves using machine learning models, data analysis tools, and neural networks to analyze scientific data, make predictions, and automate research tasks. Common tools include TensorFlow, PyTorch, and specialized platforms like DeepMind or IBM Watson, often requiring programming skills in Python and knowledge of data science. These AI applications support fields such as biology, chemistry, physics, and environmental science.

Can AI take over science jobs?

AI for science roles involves automating data analysis, modeling, and research tasks, which can enhance productivity but are unlikely to fully replace scientists. Human expertise is essential for designing experiments, interpreting results, and making complex decisions. AI tools are typically used to support scientists rather than replace them entirely.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior AI researcher, machine learning director, or AI executive, often requiring advanced skills, extensive experience, and leadership responsibilities. These roles may involve overseeing AI projects, developing innovative algorithms, and working with cutting-edge tools, and they usually offer compensation in the upper echelons of the industry. Such salaries are more common in large tech companies or specialized AI firms.

What are the key skills and qualifications needed to thrive as an AI for Science Specialist, and why are they important?

To thrive as an AI for Science Specialist, you need a strong background in computer science, mathematics, and scientific domains, often supported by advanced degrees (e.g., PhD or MSc) in relevant fields. Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools, and familiarity with high-performance computing environments are typically required. Critical thinking, interdisciplinary collaboration, and effective communication are crucial soft skills for translating scientific problems into AI solutions. These skills are vital for developing innovative models, ensuring research rigor, and enabling impactful scientific discoveries.

What is the difference between Ai For Science vs Data Scientist?

AspectAi For ScienceData Scientist
Required CredentialsDegree in Science, Computer Science, or related fields; knowledge of AI and machine learningDegree in Statistics, Computer Science, or related fields; strong programming skills
Work EnvironmentResearch labs, scientific institutions, tech companies focused on scientific applicationsCorporate, tech firms, finance, healthcare, and other industries analyzing data
Industry UsageApplied to scientific research, simulations, and experimental data analysisUsed for data analysis, predictive modeling, and business insights

Ai For Science focuses on applying AI techniques to scientific research and experiments, often requiring a background in science and specialized knowledge of AI. Data Scientists analyze large datasets across various industries to extract insights and build models. While both roles involve AI and data analysis, Ai For Science is more research-oriented within scientific contexts, whereas Data Scientists work across diverse sectors on data-driven decision making.

More about Ai For Science jobs
What cities are hiring for Ai For Science jobs? Cities with the most Ai For Science job openings:
What states have the most Ai For Science jobs? States with the most job openings for Ai For Science jobs include:
What job categories do people searching Ai For Science jobs look for? The top searched job categories for Ai For Science jobs are:
Ai For Science jobs near you
Infographic showing various Ai For Science job openings in the United States as of July 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $48,391 per year, or $23.3 per hour.

Senior / Principal Scientist, AI for Protein Engineering

Lila Sciences

San Francisco, CA

Other

Re-posted 5 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 Sciences AI (LSAI), the AI for Protein Engineering team develops and uses the generative and predictive models that drive Lila's biomolecule design programs from in silico hypothesis to wet-lab validated lead.

We are seeking a Senior or Principal Scientist to join this team as a senior IC focused on antibody design and engineering. You will develop and execute the methods and workflows that ensure successful completion of antibody campaigns. Scope may expand to additional modalities such as enzymes and peptides as needs evolve.

This role sits at the bilingual edge of ML and biology. You will own biological understanding of campaign needs and partner closely with the Life Science Research team to design and validate computational predictions in the lab. You will shape the technical agenda for AI protein engineering at Lila and represent that work both internally and to the broader research community.

What You'll Be Building

  • Develop and own protein design and engineering workflows for antibody campaigns, including de novo design, affinity maturation, and developability optimization
  • Execute design workflows end-to-end for active campaigns and deliver wet-lab-validated leads against program milestones
  • Translate campaign requirements - epitope selection, affinity targets, biophysical constraints, and developability criteria - into well-defined ML problems and design specifications
  • Adapt and extend state-of-the-art AI methods (generative models, protein language models, structure-conditioned design) to the specific demands of antibody and broader biomolecule engineering
  • Partner with the Life Science Research team on design validation, building active learning loops where wet-lab data refines and improves model performance
  • Expand the protein engineering platform to additional modalities such as enzymes and peptides as needs evolve

What You'll Need to Succeed

  • PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, or a related quantitative field
  • Proven track record of successful design of wet-lab-validated biomolecules through AI, with industry experience strongly preferred
  • Deep ML expertise with the ability to modify and adapt state-of-the-art AI approaches for protein engineering, not just apply them off-the-shelf
  • Strong fluency across both ML and protein biology, with hands-on understanding of antibody design
  • Demonstrated ability to drive a research and engineering program independently, from problem definition through experimental validation and iteration
  • Track record of close collaboration with experimental scientists and clear communication across the ML/biology boundary

Bonus Points For

  • Direct experience designing antibodies, nanobodies, or other therapeutic proteins for clinical or therapeutic pipelines
  • Experience with structure prediction, generative protein design (diffusion, flow-matching, or similar), and protein language models in a production research setting
  • Experience in structural biology and conformational dynamics
  • Experience extending design methods to additional modalities such as enzymes, peptides, or other engineered biomolecules
  • High-impact publications or open-source contributions in AI for Science (NeurIPS, ICML, ICLR, Nature Methods, Nature Biotechnology, or equivalent)
  • Experience designing or operating active learning loops between computational design and high-throughput experimental validation

Apply