Machine Learning Engineer, Bellwether, X

X, The Moonshot Factory; Mountain View (CA); On-site; USD 174,000 - 261,000; Full time

Job summary

X, The Moonshot Factory in Mountain View is seeking a hands-on Senior Machine Learning Engineer to contribute across the full lifecycle, from prototyping to production. You will work on production-grade ML systems with a focus on MLOps, observability, and scalable pipelines in a small, highly collaborative team that values pair programming and agile delivery.

You'll design and deploy ML pipelines on Google Cloud, tackle real-world climate-related applications, and help translate ambitious

Qualifications

• BS/MS in Computer Science or equivalent practical experience.
• 5+ years working across ML development, experimentation, and MLOps.
• Experience with PyTorch, TensorFlow or JAX and Python libraries (NumPy, SciPy, Pandas).
• Familiarity with Git, Apache Beam/Dataflow, Google Compute Engine and Vertex AI.
• Strong Python skills in production environments.
• Hands-on debugging, testing, and monitoring deployed apps.
• Able to thrive in an Agile, collaborative team.

Skills

Python

ML Pipelines

MLOps

Distributed training

Troubleshooting

Agile mindset

Education

BS/MS in Computer Science

Tools

Git

Apache Beam

Dataflow

Google Compute Engine

Vertex AI

Kubernetes

Job description

About X, The Moonshot Factory

X is a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions, even billions, of people. Our goal: 10x impact on the world’s most intractable problems, not just 10% improvement. We approach projects that have the aspiration and riskiness of research with the speed and ambition of a startup.

About X, The Moonshot Factory

X is a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions, even billions, of people. Our goal: 10x impact on the world’s most intractable problems, not just 10% improvement. We approach projects that have the aspiration and riskiness of research with the speed and ambition of a startup.

About The Team

Our team – Bellwether – operates at the intersection of machine learning, geospatial data, and pressing issues with enterprise customers, governments, and more. We focus on severe weather manifestations of climate change, such as wildfires, and aim to use a wide range of data and analytics to better understand and predict what these events could mean for communities and businesses across the globe. We do this through the detection and classification of the features and patterns of the natural and built worlds, the application of cutting-edge machine learning, and the redesign of the full geo-ML workflow. This work extends into development and production-level work as well, as Bellwether also has a number of products that are live with customers. The result: tools and models that allow a range of industries to better leverage earth observation insights.

About The Role

You'll be a hands-on Senior Machine Learning Engineer, contributing across the full lifecycle of building and deploying our applications, from prototyping to production. We're looking for an experienced ML engineer with a strong background in ML in production and MLOps framework for enabling continual learning. This is a highly technical Individual Contributor role focused on building real-world products, not pure research. Our team is small but mighty and highly collaborative. We value pair programming and coming up with ideas together. We follow agile principles and rely on them to run efficient sprints and cycles. We're looking for people who are comfortable moving between big-picture creative thinking and the details of execution. We work in a fast-paced, fluid environment as the team moves from early-stage development into production.

How You Will Make a 10x Impact

• Be an ML "Expert Generalist": Enjoy the fluidity of moving from architecting new ML models to production grade ML agentic systems instrumented for observability and explainability. Translate high-level strategy into specific, tactical engineering execution.
• Drive Architecture Decisions: Help drive and execute key decisions on ML pipeline architecture and features, balancing business needs with our technology roadmap, and balancing long-term system longevity with rapid prototyping.
• Build Production Systems: Contribute as a key team member to the creation of new systems and processes to ensure high-quality development, deployment, and maintenance of live applications in production environments for agentic platforms.
• Manage Cloud Infrastructure: Create and maintain Google Cloud Platform-based ML infrastructure for software development and high-volume production systems.
• Collaborate & Communicate: Present findings to team members, internal and external stakeholders, and help set the strategic direction for future development.

What You Should Have

• Education: BS/MS in Computer Science or equivalent practical experience.
• Core Experience: 5+ years working across the ML development pipeline (ML experimentation, ML development and MLOps), including scaling ML applications into robust production environments.
• ML Frameworks: Strong experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX) and Python libraries (e.g., NumPy, SciPy, Pandas).
• Open Source & Cloud Tooling: Experience with tools and infrastructure such as Git, Apache Beam/Dataflow, Google Compute Engine and Vertex AI.
• Software Engineering Craft: Strong Python skills, plus experience in an early-stage environment where prototypes evolve into production systems.
• Diagnostic Craft: Hands‑on experience troubleshooting, debugging, designing code testing processes, and monitoring the performance and health of deployed applications.
• Agile Mindset: Ability to thrive in an Agile-driven team: iteratively sprinting toward goals and products, while actively contributing new ideas, standards, and processes.
• Scaling & MLOps: Deep experience in Machine Learning Operations (MLOps) — distributed training, automated eval, model retrain/ monitoring/drift detection, data lineage, high throughput, dynamic batching etc.

It’d Be Great If You Also Had These

• Agentic/LLM Experience: Building LLM-based or agentic systems (agent frameworks, evals, tool use).
• Geospatial & Earth Observation Expertise: Production experience working with and modeling geospatial and Earth observation data (e.g., GDAL, rasterio, Earth Engine).
• Security in Production: Designing, implementing and managing security measures and controls.
• Infrastructure Tooling: Docker, ML flow, KubeFlow, Kubernetes/GKE, Terraform.

The US base salary range for this full-time position is $174,000 - $261,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.

An Equal Opportunity Workplace

At X, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please contact us at [e-mail].