Jobs / United States / General Motors Company
Entry-Level – Researcher, Aftertreatment Catalysis
General Motors Company · 🇺🇸 Warren, Michigan, United States of America
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 606 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 267 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What General Motors Company paid sponsored hires in similar roles3 certified filings for “Researcher” (Materials Engineers) in MI: $114k–$144k, median $135k. Most were filed at wage level IV (67%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
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Sponsor Radar — General Motors Company
The US Department of Labor certified 606 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 267 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for General Motors Company →
About the role
Job Description The Role: The Researcher will contribute to aftertreatment catalysis research, combining strong scientific fundamentals with practical understanding of automotive emissions systems. This individual will design and execute laboratory studies, interpret catalyst characterization data, and help translate research findings into product-relevant recommendations for catalyst formulation, structure, and system integration. The role also includes collaboration with engineering, modeling, and external research teams, with opportunities to grow technical depth through mentorship. The Team: At General Motors, we are seeking an early-career Researcher to advance aftertreatment catalysis technologies that improve emissions performance, durability, and cost competitiveness for automotive applications. This role is well suited for a researcher or recent PhD graduate who is excited to combine fundamental catalytic science, hands-on experimentation, data analytics, and artificial intelligence/machine learning (AI/ML) to develop robust catalyst designs. What You’ll Do: • Conduct research on automotive aftertreatment catalysts, with emphasis on three-way catalyst technologies and their performance, durability, and cost optimization • Design, plan, execute, and document catalyst evaluation studies using laboratory reactor systems and related experimental methods • Develop and assess catalyst structures, formulations, and architectures intended to reduce platinum group metal content while maintaining or improving conversion efficiency and durability • Apply catalyst characterization methods—including FTIR-DRIFTS, porosimetry, BET surface area analysis, chemisorption, transmission electron microscopy, and related techniques—to understand active sites, support interactions, dispersion, and aging • Correlate catalyst composition, structure, surface chemistry, and reactor performance to identify design rules for next-generation aftertreatment materials • Build and maintain high-quality datasets from experiments, literature, simulations, and characterization results, including test conditions, metadata, and data-quality information needed for reproducible modeling • Apply AI/ML methods such as feature engineering, regression, classification, clustering, dimensionality reduction, Bayesian optimization, active learning, and uncertainty quantification to guide catalyst design and experimental planning • Use interpretable and physically informed models to identify catalyst-performance relationships and prioritize designs that are robust across operating conditions, aging histories, and material variability • Close the design-build-test-learn loop by experimentally validating AI/ML-generated catalyst hypotheses and using the results to improve models and select the next experiments • Partner with modeling and simulation teams to develop predictive tools that reduce dependence on hardware-based testing and support catalyst and converter design decisions • Collaborate with universities, national laboratories, suppliers, and internal stakeholders on focused research activities, externally sponsored technical programs, technical reports, research summaries, invention disclosures, presentations, and publications, as appropriate Required Qualifications: • Pursuit/Completion of a PhD degree in one of the following areas: Chemistry, Materials Science, Chemical Engineering, Materials Engineering, or a closely related field • Must be graduating between August 2026 - June 2027 • Able to work fulltime, 40 hours per week • Research experience in heterogeneous catalysis, reaction engineering, materials chemistry, surface science, or a closely related area, with interest in automotive aftertreatment applications • Experience