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Ai For Science Jobs (NOW HIRING)

AstraZeneca

Executive Director, AI for Discovery

Boston, MA · On-site

$23K/mo

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Role Overview As Executive Director, AI for Discovery within the AI for Science Innovation Unit, you will lead AstraZeneca's strategy for applying advanced AI and machine learning to accelerate ...

AstraZeneca

Executive Director, AI for Discovery

Boston, MA · On-site

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Role Overview As Executive Director, AI for Discovery within the AI for Science Innovation Unit, you will lead AstraZeneca's strategy for applying advanced AI and machine learning to accelerate ...

AstraZeneca GmbH

Executive Director, AI for Discovery

Boston, MA · On-site

$258 - $387/hr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Role Overview As Executive Director, AI for Discovery within the AI for Science Innovation Unit, you will lead AstraZeneca's strategy for applying advanced AI and machine learning to accelerate ...

Polymathic Ai

Research Scientists

Manhattan, NY · On-site

$120 - $250/hr

Conduct cutting-edge research at the intersection of AI and science Develop large-scale deep learning models for scientific discovery Work closely with experts in machine learning, domain sciences ...

New

UAB

SCIENTIST III

Tampa, FL

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 ...

University of Alabama at Birmingham

SCIENTIST III

Tampa, FL · On-site

$80.30 - $133.30/hr

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 ...

New

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Ai For Science information

See salary details

$24.5K

$48.4K

$79K

How much do ai for science jobs pay per year?

As of Aug 19, 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.

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.

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.

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.

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Ai For Science jobs near you
Infographic showing various Ai For Science job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, and 3% Contract. Highlights an 74% Physical, 4% Hybrid, and 22% Remote job distribution, with an average salary of $48,391 per year, or $23.3 per hour.

Co-Op, ML Scientist for Biology

Lila Sciences

San Francisco, CA

Full-time

Re-posted 25 days ago

Job description

Your Impact at LILA

Lila is building a platform where AI and automation co-evolve to solve hard problems across scientific domains. Within Life Sciences AI, we are developing autonomous-science capabilities for biological systems, spanning multiple biological domains and resolutions, based on multi-modal data and foundation models.

We are seeking a Co-Op, LS AI, ML Scientist for Biology to contribute to cutting-edge research on how to effectively evaluate, guide, and reinforce agentic model behavior in this domain.

This is an opportunity to work alongside Lila scientists on early-stage research in autonomous life science AI. You will help explore reasoning models, evaluation and benchmark datasets, and workflows that connect modern AI methods to real biological questions, gaining hands-on experience in a fast-moving scientific environment.

What You'll Be Building

  • Contribute to ML research on reasoning models for biological discovery and autonomous science.
  • Explore methods to evaluate, guide, and reinforce agentic model behavior in biological domains.
  • Help develop evaluation and benchmark datasets for biological reasoning tasks.
  • Analyze multi-modal biological data to identify useful signals for model evaluation and improvement.
  • Prototype workflows that connect model reasoning, evaluation, and scientific feedback.
  • Communicate findings through code, notebooks, written summaries, and presentations.

What You'll Need to Succeed

  • Currently enrolled in a PhD program in Computer Science, Machine Learning, Computational Biology, Bioengineering, or a related quantitative field.
  • Research experience in machine learning, AI for science, computational biology, or biological data analysis.
  • Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, or similar tools.
  • Experience working with biological, scientific, or multi-modal datasets.
  • Interest in reasoning models, agentic systems, evaluation methods, or benchmark design.
  • Interest in closed-loop scientific discovery, autonomous labs, or AI systems that interact with experimental feedback.
  • Ability to communicate research findings clearly through code, notebooks, written summaries, and presentations.
  • Comfort working in a collaborative, cross-disciplinary research environment.

Bonus Points For

  • Experience with reasoning models, agentic systems, reinforcement learning, or model evaluation.
  • Experience developing benchmarks, evaluation datasets, or model assessment workflows.
  • Publications, preprints, talks, posters, or workshop presentations in ML, AI for science, computational biology, or related scientific venues.

Apply