What you'll do

As Reflective's Senior Scientist, Observing Systems & Inverse Modeling, you will lead the design of observation strategies and inverse modeling workflows to determine what measurements are needed for SAI field experiments.

This is a specialized scientific role, not a general data science, analytics, or machine-learning role. Strong candidates will have used observational data from atmospheric, environmental, geophysical, laboratory, or other physical systems to evaluate, constrain, tune, or improve models.

This role sits at the intersection of atmospheric observations, aerosol microphysics, inverse modeling, data assimilation, and field campaign design. You'll develop methods to use existing high-quality observational datasets - including SABRE, AToM, and other relevant missions - to improve microphysical model parameterizations. You'll then use those methods to determine which observations matter most, what minimum instrument suite is needed for an outdoor experiment, and how many experimental iterations may be required to meaningfully constrain model uncertainty. Your work will be fundamental to field experiment design, and you will have primary responsibility for data analysis and model optimization after an experiment has been conducted.

Responsibilities

• Develop an inverse modeling framework to use SABRE, AToM, and other relevant in situ observational datasets to improve existing aerosol microphysical models, potentially including adjoint-based approaches.
• Design and run data denial experiments to determine which observations are most important for constraining microphysical parameters to help define a minimum viable instrument suite for a future outdoor field experiment.
• Develop formal Observing System Simulation Experiments (OSSEs) that simulate observations of an aerosol plume under different potential instrument suites, sampling strategies, and cadences to quantify the marginal value of different measurement strategies.
• Repeat OSSE analyses across a range of plume conditions, atmospheric states, and experimental configurations, and translate OSSE results into practical field campaign recommendations: where to sample, how often, at what altitude, with which instruments, and with what acceptable error bounds.
• Build data-processing workflows for future scientific field-experiment data, ensuring that data can be rapidly quality-controlled, analyzed, and used to update model parameterizations.
• Work closely with Reflective's science, engineering, and data teams to translate model uncertainty into concrete observational requirements.
• Write scientific papers, concise memos, technical documentation, and public-facing summaries that make what has been learned, what remains uncertain, and how the results should inform experiment design clear to funders, policymakers, researchers, and the wider field.

Who you are

Minimum qualifications

• PhD in atmospheric science, aerosol science, applied math, engineering, Earth system science, or a related field.
• Significant experience working with atmospheric observational datasets, especially in situ data from aircraft, field campaigns, or comparable observing systems.
• Experience with inverse modeling, data assimilation, optimization, uncertainty quantification, or a closely related quantitative method.
• Strong scientific programming skills, especially in Python, and experience working with large, complex environmental datasets.
• Strong quantitative judgment, including the ability to reason about nonlinear systems, over-constrained inference problems, parameter identifiability, and model structural uncertainty.
• Ability to design rigorous numerical experiments that connect technical modeling choices to real-world observing requirements.
• Excellent written and verbal communication skills, especially with mixed scientific, engineering, and non-technical audiences.
• You're creative and attached to outcomes, not process - you're constantly looking for new paths to the destination and excited to switch gears if there's a faster, better way to get something done.
• You are low ego, and have a proven track record for working well across disciplines and with external partners.
• You are passionate about Reflective's mission.

Strongly preferred

• Experience with adjoint methods, variational data assimilation, gradient-based optimization, or other approaches for high-dimensional parameter estimation.
• Familiarity with datasets from SABRE, AToM, or similar atmospheric chemistry / aerosol missions.
• Experience designing or running OSSEs, OSEs, data denial experiments, or observing network optimization studies.
• Experience with JAX or with building adjoints with automatic differentiation

Not needed

• Prior work on sunlight reflection. We care more about the underlying technical skillset, scientific judgment, and ability to learn quickly.
• A nonprofit re9sume9. Reflective is technically a nonprofit, but it doesn't feel like one - mission fit and rigor matter more than sector pedigree.
• Direct experience with every part of the workflow. We do not expect one person to have done this exact problem before; in fact, we know the full version of this problem has not yet been solved.
• Experience with every relevant observational dataset, instrument, or model. We need someone who can learn quickly, build the right framework, and work well with domain experts.

Location

Our goal is to hire the right person for the role regardless of location, but we have a slight preference for candidates who can work from our Bay Area office 2-3 days/week. However, the role can be fully remote and we are open to candidates based anywhere in the world who can overlap with our core working hours (9am-1pm PST). We may be able to sponsor visas for US-based foreign nationals and have a moving stipend to support candidates who would like to relocate to the Bay Area.

Regardless of location, we love seeing each other in person and believe regular co-location helps improve collaboration and team culture. As such, we plan regular team co-working weeks, typically in the Bay Area.

Compensation and Benefits

We are committed to providing competitive compensation and comprehensive benefits to our employees. We offer fixed salary levels based on experience and role to minimize biases in compensation and to ensure team members are paid the same for doing the same work.

We expect this position to be a regular, full-time position, with an annual salary between $130,000 and $180,000 USD, depending on level of experience. In addition to salary, we offer a comprehensive set of benefits to all full-time employees:

• Medical, dental, vision insurance
• 401(k)
• Professional and personal development
• Generous paid time off and sick leave, including 12 weeks paid parental leave
• Flexible working hours

How to Apply & Interview Process

We encourage candidates to apply even if they do not meet every preferred qualification. However, this is a specialized scientific role. The strongest candidates will have substantial experience using observational data from physical systems to evaluate, constrain, tune, or improve models. Applications that do not demonstrate this experience in the required application questions are unlikely to advance.

We are accepting applications between now and August 9th at midnight PST. Our interview process includes the following:

• First round interview (30-45 min)
• Take home assessment (max 2 hours)
• Panel interview (1-1.5 hr)
• Final round interview (30-45 min)

We are hoping to make an offer by by mid-September or sooner, with a target start date of mid-October. We will kickoff the above interview process once all applications have been received and reviewed.