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About This Role
WindBorne Systems designs, manufactures, and operates Atlas, a global constellation of smart weather balloons. The atmospheric observations they collect help drive WeatherMesh, our AI weather model.
Our model, WeatherMesh, runs 24/7, producing global forecasts every hour. Our deep learning research team is small and iterates quickly, but sometimes R&D gets ahead of the internal tools that support it. We’re looking for a talented and curious software engineer to keep model inference running reliably, process atmospheric data as it arrives, and build tools that show us what’s happening in both.
In this role, you’ll work with our deep learning team and backend and frontend engineers. We have problems for you to tackle right away, and we also want someone with ideas about what to build next. You’ll help figure out what’s needed, build it, and be responsible for how it works once it’s running.
Prior machine learning experience is not required! You can come in with no ML background at all and be successful. Where you lack understanding, you’ll dig into model code, ask researchers questions, and use AI tools to get up to speed. Essentially, learn what you need to build and go build it. We care much more about how well you learn and your approach to problem solving than whether you have RAG or LLM fine-tuning on your resume. [Note: We’re not trying to shade ML engineers - we have many great ones on our team. Yes, those skills can be useful in other domains, but they’re not prerequisites for this role. We’re bringing them up to emphasize that, without good software taste and a knack for learning, ML skills alone are not what we're looking for.]
What you’ll own
- Keep forecasts running. Build the tooling that runs our weather models and detects failures. If a cloud node goes down, our systems need to recover and keep producing forecasts.
- Process atmospheric data in real time. Build pipelines for incoming observations, account for late or missing data, and make it clear when something needs attention.
- Show us what’s happening. Build tools to visualize data processing and model inference as they run. The team should be able to see how a run is progressing, where it’s slowing down, and what went wrong if it fails.
You’d be a good fit if:
- You’ve owned software in production. You’ve been responsible for a system after launch: how it performs, how it fails, and how it recovers. That experience has given you a feel for the monitoring, reliability, and operational discipline a critical system needs.
- You’ve built substantial software. You can walk us through a hard technical problem you solved, why you chose your approach, and what you learned. You’re comfortable reading unfamiliar code and tracking down bugs that involve more than one part of a system. Bonus points for good UI design and a portfolio we can see.
- You think beyond the immediate fix. You can solve today’s problem while recognizing what should become reusable infrastructure for future work. You have ideas about how to improve the systems you own and want to help decide where we go next.
- You’re curious and motivated to build. You ask questions, investigate how things work, and turn what you learn into something useful. You don’t sit idly while waiting for guidance; you can’t help but poke around.
- You use AI and agentic tools like a bandit. You use them everywhere you can: to explore code, learn about unfamiliar systems, test ideas, and build quickly. You understand the code you ship and can debug it when it doesn’t work. Most importantly, you know these tools are powerful, but you can’t outsource thinking to them entirely. After all, you’re still on the hook for what gets built.
- You want to work on a dynamic team and see your work put to use quickly.
We’re flexible on programming languages, frameworks, and years of experience. Show us what you’ve built and how you think through problems. You can learn the stack here, and honestly, things change quickly.
Nice to have
- Experience with data pipelines, distributed systems, or infrastructure.
- Experience with scientific computing, weather data, or geospatial systems.
- Familiarity with ML inference, GPU computing, or PyTorch (again, not required).
Compensation and location
- Base salary: $120,000–$200,000, depending on experience and scope.
- Location: In person at 1600 Bridge Parkway, Redwood Shores, CA.
- Benefits: 401(k), health, dental and vision insurance, unlimited PTO, stock options, and office food and beverages.
Salary Context
This $120K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At WindBorne Systems, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($160K) sits 25% below the category median. Disclosed range: $120K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
WindBorne Systems AI Hiring
WindBorne Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palo Alto, CA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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