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Machine Learning Research Engineer, Model Evaluation

WindBorne Systems · On-site

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Last seen by MeritLog September 8, 2026Source: AshbySource version: ashby-public-job-posting-v1

MeritLog read this listing from WindBorne Systems's Ashby job board and last checked it on September 8, 2026.

Source: the employer's Ashby job board. Open the original listing for current details.

Job details

Work model
On-site
Salary
$140,000 – $240,000
Location
RWC HQ
Company website
arxiv.org

Hiring context

How this role compares at WindBorne Systems

WindBorne Systems has 21 live roles in MeritLog’s catalog across 3 job families, and 13 of them are in data & analytics. 2 of those listings publish a pay range, a disclosure rate of 10%.

WindBorne Systems concentrates this hiring in:

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

What you'd do

  • Evaluation strategy - Work with our Meteorology team to develop a rigorous, meteorologically valid strategy for comparing WeatherMesh with leading AI and physics-based models. Choose the metrics, datasets, baselines, and case studies that provide an honest picture of forecast quality.
  • Fast feedback and durable systems - Build quick evaluations that give researchers useful signals, then turn recurring analyses into reliable, reusable infrastructure. Think systematically about reproducibility, provenance, and how evaluation tools fit into the broader research workflow.
  • Agentic tooling for evals - Improve our existing evaluation infrastructure, including agentic AI-based tools for investigating forecasts and synthesizing results.
  • Technical communication - Produce clear scorecards, visualizations, and explanations for researchers, leadership, customers, and external partners. Communicate model performance precisely, including uncertainty and important caveats.

What they're asking for

  • Excellent scientific judgment and healthy skepticism. You ask whether a comparison is fair, what else could explain a result, and what evidence would change your mind.Skill
  • Strong experimental taste: you can identify the evaluation that answers the question that matters and distinguish robust improvement from noise.Skill
  • Systems thinking: you can solve today’s problem while recognizing what should become reusable infrastructure for future work.Skill
  • Experience evaluating ML systems using large, scientific, geospatial, multidimensional, or time-series datasets.Skill
  • Strong Python skills and experience with scientific and ML tools such as PyTorch, NumPy, pandas, or xarray.Skill
  • Able to investigate ambiguous results independently, synthesize evidence, and communicate conclusions clearly.Skill
  • Experience with weather, climate, forecasting, physical science, or AI-assisted research tools is helpful, but not required.SkillPreferred

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

MACHINE LEARNING RESEARCH ENGINEER, MODEL EVALUATION WindBorne Systems is supercharging weather forecasts with a proprietary data source: a global constellation of next-generation smart weather balloons targeting critical atmospheric data. We design, manufacture, and operate our own balloons, using their observations to generate otherwise unattainable weather intelligence. Our mission is to eliminate weather uncertainty and help humanity adapt to climate change-whether by predicting hurricanes or speeding the adoption of renewables. The founding team of Stanford engineers was named Forbes 2019 30 Under 30 and is backed by top-tier investors, including Khosla Ventures and Footwork VC. WindBorne builds AI weather models that run 24/7, producing global forecasts every 20 minutes. Evaluating these models is much harder than producing a headline accuracy number: performance varies across regions, lead times, weather regimes, and customer use cases, while standard metrics often fail to capture what makes a forecast meteorologically sound or useful. We need someone with excellent scientific taste to determine where our models excel, where they fail, and which results we should trust. You will work at the intersection of machine learning and meteorology, combining fast analyses with robust systems that make future research faster and more reliable. RESPONSIBILITIES WHAT YOU’D OWN: - Evaluation strategy - Work with our Meteorology team to develop a rigorous, meteorologically valid strategy for comparing WeatherMesh with leading AI and physics-based models. Choose the metrics, datasets, baselines, and case studies that provide an honest picture of forecast quality. - Fast feedback and durable systems - Build quick evaluations that give researchers useful signals, then turn recurring analyses into reliable, reusable infrastructure. Think systematically about reproducibility, provenance, and how evaluation tools fit into the broader research workflow. - Agentic tooling for evals - Improve our existing evaluation infrastructure, including agentic AI-based tools for investigating forecasts and synthesizing results. - Technical communication - Produce clear scorecards, visualizations, and explanations for researchers, leadership, customers, and external partners. Communicate model performance precisely, including uncertainty and important caveats. SKILLS AND QUALIFICATIONS REQUIREMENTS - Excellent scientific judgment and healthy skepticism. You ask whether a comparison is fair, what else could explain a result, and what evidence would change your mind. - Strong experimental taste: you can identify the evaluation that answers the question that matters and distinguish robust improvement from noise. - Systems thinking: you can solve today’s problem while recognizing what should become reusable infrastructure for future work. - Experience evaluating ML systems using large, scientific, geospatial, multidimensional, or time-series datasets. - Strong Python skills and experience with scientific and ML tools such as PyTorch, NumPy, pandas, or xarray. - Able to investigate ambiguous results independently, synthesize evidence, and communicate conclusions clearly. - Experience with weather, climate, forecasting, physical science, or AI-assisted research tools is helpful, but not required. BENEFITS - 401(k) - Dental, health, and vision insurance - Unlimited PTO - Stock Option Plan - Office food and beverages SALARY - $140k–$240k. We consider a range of backgrounds and experience levels and adjust offers to be competitive with market rates. LOCATION 1600 Bridge Pkwy, Redwood City, CA. Hybrid or in-person.

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