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Science Policy Advisor Jobs in California (NOW HIRING)

... Policy Advisor to four US presidents. What You'll Work On * Develop and scale MPI+CUDA PDE solvers ... Solid understanding of AI for Science methodology: how to design datasets from simulations, handle ...

... Policy Advisor to four US presidents. What You'll Work On * Develop and scale MPI+CUDA PDE solvers ... Solid understanding of AI for Science methodology: how to design datasets from simulations, handle ...

... science, or related discipline) * 5+ years of experience with privacy, data security, or technology policy issues * Experience counseling and advising on privacy or other technology policy issues ...

... law, political science, or related discipline) • 5+ years of experience with privacy, data security, or technology policy issues • Experience counseling and advising on privacy or other ...

Serve as a student advisor * Design and lead Experiential Electives * Lead an Drew Education for ... NON-DISCRIMINATION POLICY Drew School of San Francisco admits students of every race, color ...

Strategic Projects Lead

Menlo Park, CA · On-site

$182K - $191K/yr

... Policy Advisor to four US presidents. About this Role Voltai Inc. is seeking a Strategic Projects ... Science, Physics, or a related field, and four years of experience in the job offered, or in an ...

Strategic Projects Lead

Menlo Park, CA · On-site

$182K - $191K/yr

... Policy Advisor to four US presidents. About this Role Voltai Inc. is seeking a Strategic Projects ... Science, Physics, or a related field, and four years of experience in the job offered, or in an ...

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Science Policy Advisor information

See California salary details

$43.4K

$103.4K

$154.4K

How much do science policy advisor jobs pay per year?

As of Aug 27, 2026, the average yearly pay for science policy advisor in California is $103,388.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,000.00 and $125,800.00 per year, depending on experience, location, and employer.

What does a Science Policy Advisor do?

A Science Policy Advisor provides expertise at the intersection of science and public policy. They analyze scientific research, interpret data, and advise government agencies, non-profits, or private organizations on the implications for policy decisions. Their work often involves translating complex scientific concepts for policymakers, drafting policy briefs, and participating in discussions about regulations or funding priorities. Science Policy Advisors play a crucial role in ensuring that policies are informed by the latest scientific evidence.

How does a Science Policy Advisor typically collaborate with researchers and government officials to shape effective policy recommendations?

Science Policy Advisors frequently act as liaisons between the scientific community and policymakers, translating complex research findings into actionable policy insights. They regularly attend meetings with both researchers and government stakeholders, facilitate discussions to ensure scientific evidence is accurately represented, and draft policy briefs that address both scientific and legislative priorities. This role requires strong communication and negotiation skills, as well as the ability to balance scientific integrity with practical policy considerations. Collaboration is often cross-disciplinary, involving teams from various scientific and governmental backgrounds.

What are the key skills and qualifications needed to thrive as a Science Policy Advisor, and why are they important?

To thrive as a Science Policy Advisor, you need a strong background in scientific research or policy analysis, typically supported by an advanced degree in science, public policy, or a related field. Familiarity with data analysis tools, government policy frameworks, and experience drafting policy briefs or regulatory documents are important technical qualifications. Outstanding communication, critical thinking, and stakeholder engagement skills help advisors effectively translate complex science into actionable policy recommendations. These skills and qualities are crucial for ensuring that policy decisions are evidence-based and align with scientific best practices.

What is the difference between Science Policy Advisor vs Science Communications Specialist?

AspectScience Policy AdvisorScience Communications Specialist
Required credentialsTypically advanced degrees in science or policyBackground in science, journalism, or communications
Work environmentGovernment agencies, think tanks, policy organizationsMedia outlets, research institutions, NGOs
Employer and industry usagePolicy development, legislative advisingPublic outreach, media engagement

The Science Policy Advisor focuses on shaping science-related policies and advising government or organizations, often requiring a strong background in science and policy. In contrast, the Science Communications Specialist emphasizes translating scientific information for public understanding through media and outreach. Both roles require science literacy but serve different functions within the science sector.

What are popular job titles related to Science Policy Advisor jobs in California?

For Science Policy Advisor jobs in California, the most frequently searched job titles are:

What job categories do people searching Science Policy Advisor jobs in California look for?

The top searched job categories for Science Policy Advisor jobs in California are:

What cities in California are hiring for Science Policy Advisor jobs?

Cities in California with the most Science Policy Advisor job openings:

Infographic showing various Science Policy Advisor job openings in California as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, and 3% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $103,388 per year, or $49.7 per hour.

Computational Scientist

Palo Alto, CA • On-site

Full-time

Re-posted 8 days ago


Job description

About Voltai
Voltai is developing world models, and agents to learn, evaluate, plan, experiment, and interact with the physical world. We are starting out with understanding and building hardware; electronics systems and semiconductors where AI can design and create beyond human cognitive limits.
About the Team
Backed by Silicon Valley's top investors, Stanford University, and CEOs/Presidents of Google, AMD, Broadcom, Marvell, etc. We are a team of previous Stanford professors, SAIL researchers, Olympiad medalists (IPhO, IOI, etc.), CTOs of Synopsys & GlobalFoundries, Head of Sales & CRO of Cadence, former US Secretary of Defense, National Security Advisor, and Senior Foreign-Policy Advisor to four US presidents.
What You'll Work On
  • Develop and scale MPI+CUDA PDE solvers for electrostatics, charge transport, and electromagnetic field problems on complex 3D IC geometries across multi-node GPU clusters
  • Tune and extend AMG preconditioners, Krylov solvers, and mesh pipelines for performance and correctness at scale
  • Build and train neural operators (FNO, DeepONet, GNO, and variants) as high-fidelity surrogates for PDE-based field solvers
  • Design simulation pipelines that generate training data for neural operator models - including sampling strategies, mesh handling, and physical consistency checks
  • Validate everything: analytical solutions, published benchmarks, and cross-validation between field solvers and learned surrogates

Required
  • PhD in computational physics, applied mathematics, computational engineering, or a closely related field
  • Deep expertise in numerical PDE methods: FEM, FVM, or BEM - weak formulations, quadrature, convergence, error analysis
  • Strong C++ and CUDA - writing and optimizing kernels, memory hierarchy, multi-GPU programming
  • Multi-node HPC: MPI, domain decomposition, collective communication, strong/weak scaling
  • Sparse linear algebra at depth: Krylov methods, algebraic multigrid, preconditioning strategies
  • Hands-on experience with neural operators (FNO, DeepONet, or equivalent) - training, architecture design, and evaluation on PDE datasets
  • Solid understanding of AI for Science methodology: how to design datasets from simulations, handle out-of-distribution generalization, and ensure physical consistency of learned models

Strongly Preferred
  • Experience with HYPRE, PETSc, and Trilinos
  • Familiarity with multi-node GPU clusters: NCCL, CUDA-aware MPI, NVLink topologies
  • Published work in neural operators, physics-informed ML, or scientific HPC
  • IC design domain knowledge: device physics, semiconductor materials, layout data formats