About SolidSky
SolidSky is on a mission to enable the decarbonization of chemical plants by making decarbonization more profitable and compatible with their existing systems. We develop platforms and materials that enable the rapid, profitable decarbonization of $4T+ worth of legacy infrastructure, and know this is the fastest way to get to net zero. We are a spinout from the Lawrence Berkeley National Lab, and are working on the hardest, most pressing industrial decarbonization challenges with the best tools on Earth. We are looking for motivated builders to join us on our mission.
The Role
We're seeking a Founding Head of Research to lead our catalysis discovery and optimization efforts. You'll optimize our existing formulations, develop new ones based on thesis-driven discovery and customer feedback, and build our high-throughput experimentation (HTE) and computational materials platforms from the ground up. This is a rare opportunity to define the technical culture and infrastructure of a climate tech startup at its earliest stages, with direct impact on our ability to scale carbon negative reactions to industrial relevance.
What You'll Achieve
Catalyst Discovery Programs
- Drive systematic exploration of SolidSky's core portfolio of catalysts, optimizing for improved performance characteristics and IP differentiation
- Analyze state-of-the-art literature and breakthroughs and translate them to our offerings
- Develop and test hypotheses for relevant expansions of our capabilities through translation of theory to outcomes
- Develop and optimize novel catalyst formulations that align with our mission and customer needs
- Translate discovery-stage leads into specifications for pilot-scale validations
HTE Platform
- Design and commission custom rig designed for parallel testing and characterization
- Establish automated workflows for screening catalysts across C1–C8 hydrocarbon transformations
- Build experimental design frameworks spanning catalyst synthesis variables, operating conditions, and regeneration cycles to ensure well structured data in a clear problem space
- Develop rapid catalyst screening protocols delivering conversion/selectivity/stability metrics with statistical rigor
Computational Materials Infrastructure
- Select and deploy modeling toolchain and hypothesis generation frameworks
- Develop descriptor frameworks linking catalyst structure to performance
- Implement surrogate modeling approaches across chemistry clusters
- Establish active learning and Bayesian optimization pipelines to guide campaigns
- Create feedback loops enabling computational predictions to accelerate empirical validation from months to weeks
Team & Capability Building
- Recruit and mentor research team spanning experimental catalysis, HTE automation, computational chemistry, and data science
- Establish lab culture balancing rapid iteration with mechanistic rigor and reproducibility standards
- Build training programs enabling team members to operate across experimental and computational domains
Ideal Candidate Profile
Required Experience
- PhD in Chemical Engineering, Chemistry, Materials Science, or related field with focus on catalysis or materials discovery
- 3–7 years post-PhD experience designing and implementing HTE systems OR computational materials workflows in industrial or startup settings
- Background in dehydrogenation, oxidative dehydrogenation, reforming, or CO₂ utilization chemistry
- Demonstrated track record building research capabilities from scratch (0→1), including equipment specification, vendor selection, commissioning, and validation
- Hands-on expertise in heterogeneous catalysis, including catalyst synthesis, characterization (XRD, BET, TPR/TPD, microscopy), and performance testing
- Experience in molecular dynamics and computational modeling for screening and discovery of new materials
- Deep familiarity with gas-phase catalytic reactors and associated analytics (GC/MS, online analysis)
- History of working in fast-paced industrial R&D or startup environments with tight timelines and resource constraints
- Track record hiring, mentoring, and developing technical talent
Strong Plus Factors
- Direct experience with automated catalyst screening platforms
- Expertise in active learning, Bayesian optimization, or machine learning approaches to materials discovery
- Knowledge of patent strategy and freedom-to-operate analysis for catalyst compositions
- Grant writing experience (DOE, ARPA-E, NSF) and comfort communicating technical progress to non-technical stakeholders