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Hybrid Science Jobs in California (NOW HIRING)

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Hybrid Science information

What is a hybrid scientist?

A Hybrid Scientist is a professional who combines expertise from multiple scientific disciplines, often integrating skills from fields such as biology, computer science, engineering, or data analytics. They use interdisciplinary approaches to solve complex problems that cannot be addressed by a single traditional field. Hybrid Scientists are commonly found in areas like biotechnology, environmental science, and artificial intelligence, where the blending of knowledge leads to innovative solutions. Their unique skill set makes them valuable in research, development, and product innovation roles.

What are the key skills and qualifications needed to thrive as a hybrid scientist?

To thrive as a Hybrid Scientist, you need a strong interdisciplinary background combining life sciences, computational skills, and analytical problem-solving, often supported by advanced degrees in fields like bioinformatics, computational biology, or data science. Familiarity with programming languages (e.g., Python, R), data analysis tools, laboratory techniques, and relevant certifications such as GLP or GCP are typically required. Excellent communication, adaptability, and collaboration skills distinguish top performers in this role. These abilities are crucial for integrating diverse scientific domains, driving innovation, and effectively contributing to complex, cross-functional research projects.

How does a hybrid science professional typically collaborate with interdisciplinary teams, and what challenges might arise?

Hybrid Science professionals often work at the intersection of multiple scientific disciplines, such as biology, chemistry, and data science. Collaboration involves frequent communication with specialists from different backgrounds to integrate diverse methodologies and perspectives. One common challenge is bridging terminology gaps and aligning goals across disciplines, which requires strong interpersonal and project management skills. Successfully navigating these collaborations not only advances projects but also broadens your expertise and opens up opportunities for leadership roles.

What is the difference between Hybrid Science vs Data Scientist?

AspectHybrid ScienceData Scientist
Required CredentialsScience degrees, certifications in data analysis, programmingStatistics, computer science, data analysis certifications
Work EnvironmentResearch labs, industry settings, interdisciplinary teamsTech companies, research firms, consulting
Employer & Industry UsageResearch institutions, biotech, environmental agenciesTech, finance, healthcare, marketing

Hybrid Science professionals combine scientific expertise with data analysis skills to interpret complex data in research and industry. Data Scientists focus primarily on analyzing large datasets to inform business decisions. While both roles require strong analytical skills and technical knowledge, Hybrid Science emphasizes interdisciplinary scientific understanding alongside data analysis, making it ideal for research-driven environments.

What cities in California are hiring for Hybrid Science jobs?

Cities in California with the most Hybrid Science job openings:

Infographic showing various Hybrid Science job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 17% Part Time, and 2% Contract. Highlights an 73% Physical, 4% Hybrid, and 23% Remote job distribution.

Hybrid | Senior Materials Scientist - $60-$100/hour

24-MAG LLC

San Francisco, CA • Hybrid

$60 - $100/hr

Full-time

This job post has expired 2 days ago. Applications are no longer accepted.


Job description

About the job Hybrid | Senior Materials Scientist - $60-$100/hour

We are sharing a specialised full-time opportunity for senior materials science and engineering professionals with substantial research or industrial R&D experience across advanced materials, characterisation, simulation, and materials development.

This role supports advanced research focused on improving how AI systems reason through real-world materials science and engineering work. Selected professionals will review complex technical tasks and outputs, develop authoritative reference solutions and instructions, and translate expert materials judgement into rigorous standards for scientifically sound problem-solving.

Key Responsibilities

Materials Science Quality Review

  • Evaluate materials science and engineering tasks for technical accuracy, completeness, and professional realism
  • Review outputs for unsupported structure-property relationships, weak mechanistic reasoning, flawed assumptions, and missing considerations
  • Identify conclusions that appear plausible but would not withstand expert technical scrutiny
  • Assess whether interpretations are appropriately supported by experimental, computational, or literature evidence
  • Apply senior-level judgement across complex materials problems

Structure, Properties & Performance

  • Develop and review tasks involving relationships between material structure, composition, processing, and performance
  • Evaluate mechanical, thermal, electrical, electrochemical, optical, and related material properties
  • Assess whether proposed mechanisms are consistent with observed behaviour
  • Identify gaps between experimental evidence and claimed conclusions
  • Produce reference solutions grounded in established materials science principles

Energy Storage & Battery Materials

  • Contribute expertise to scenarios involving electrode materials, electrolytes, interfaces, degradation, and performance
  • Evaluate material-selection and optimisation decisions
  • Assess electrochemical behaviour and structure-property relationships
  • Identify unsupported claims around lifetime, stability, safety, or performance
  • Apply practical research judgement to battery and energy-storage problems

Semiconductors & Electronic Materials

  • Develop and review tasks involving semiconductor materials, thin films, electronic properties, and device-relevant behaviour
  • Evaluate processing, characterisation, and materials-selection decisions
  • Assess defects, interfaces, transport properties, and structure-function relationships
  • Identify inconsistencies between physical mechanisms and proposed interpretations
  • Apply relevant materials and applied-physics knowledge

Polymers, Soft Matter & Structural Materials

  • Review scenarios involving polymers, composites, soft materials, alloys, and metallurgy
  • Evaluate processing-microstructure-property relationships
  • Assess phase behaviour, deformation, failure, durability, and performance
  • Develop technically realistic materials-selection and optimisation tasks
  • Apply practical understanding of industrial and research workflows

Characterisation & Microscopy

  • Develop and evaluate tasks involving microscopy, spectroscopy, diffraction, and other materials-characterisation methods
  • Interpret experimental outputs and determine whether conclusions are supported
  • Assess method selection, sample preparation, limitations, and artefacts
  • Identify when additional characterisation or validation is required
  • Translate practical laboratory judgement into clear evaluation criteria

Computational Materials & Simulation

  • Review problems involving computational materials science, modelling, and simulation
  • Evaluate assumptions, boundary conditions, numerical approaches, and interpretation of results
  • Assess whether computational outputs align with physical expectations
  • Develop tasks involving simulation-driven materials analysis or optimisation
  • Identify numerical or modelling limitations that materially affect conclusions

Instruction & Reference Solution Development

  • Write detailed task instructions reflecting authentic materials science workflows
  • Produce high-quality reference solutions to complex technical problems
  • Define what constitutes a complete, scientifically credible, and professionally defensible response
  • Translate tacit materials expertise into explicit evaluation criteria
  • Develop new tasks based on realistic research and industrial decision-making

Quality Standards & Calibration

  • Collaborate with research teams and specialists from adjacent scientific disciplines
  • Help maintain consistent standards across materials-related evaluation work
  • Identify recurring weaknesses and failure patterns in model outputs
  • Refine evaluation criteria based on observed performance
  • Contribute domain expertise to new materials-focused research workflows

Ideal Profile

  • PhD in Materials Science, Materials Engineering, or a closely related discipline such as Chemistry, Chemical Engineering, Applied Physics, or Metallurgy
  • Master's degree may be considered for candidates with exceptional industrial depth
  • 4+ years of substantive research or industrial R&D experience in materials science or engineering
  • Professional experience within a research university, national laboratory, industrial R&D organisation, or comparable environment
  • Genuine specialisation in at least one area such as:
    • Energy storage and battery materials
    • Semiconductors and electronic materials
    • Polymers and soft matter
    • Structural alloys and metallurgy
    • Characterisation and microscopy
    • Computational materials and simulation
  • Clear progression into senior responsibility, such as Senior Scientist, Staff Scientist, Research Lead, Principal Investigator, or comparable senior industrial R&D role
  • Demonstrated ownership of research direction, development programmes, or major technical workstreams
  • Peer-reviewed publications, granted patents, or commercially deployed materials programmes are highly valued
  • Practical experience using large language models in professional or research workflows
  • Strong judgement when distinguishing rigorous scientific reasoning from superficially plausible conclusions
  • Excellent written communication and ability to provide precise, structured technical feedback

Engagement Details

  • Full-time position
  • Hybrid - Bay Area, California
  • Expected commitment of 40 hours per week
  • Initial engagement of approximately 6 months
  • Compensation: $60-$100/hour
  • Candidates must live in the Bay Area and be available to work on-site with the assigned team multiple days per week when required
  • Candidates not currently based in the Bay Area must be willing to relocate there at their own expense before the engagement begins
  • Relocation assistance is not provided
  • Client-issued accounts and equipment may be provided for work within designated systems and workflows
  • Projects may be extended, shortened, or concluded based on organisational needs and performance

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy.