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.
Skills Required
- BS or MS in Computer Science, or equivalent practical experience
- 5+ years of experience across the machine learning development pipeline, including experimentation, development, MLOps, and production scaling
- Strong experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX
- Experience with Python libraries such as NumPy, SciPy, and Pandas
- Experience with Git, Apache Beam or Dataflow, Google Compute Engine, and Vertex AI
- Strong Python software engineering skills
- Experience evolving prototypes into production systems in an early-stage environment
- Hands-on troubleshooting, debugging, code testing, and monitoring of deployed applications
- Ability to work effectively in an Agile-driven team and contribute to sprints, standards, and processes
- Deep MLOps experience, including distributed training, automated evaluation, retraining, monitoring, drift detection, data lineage, high throughput, and dynamic batching
- Experience building LLM-based or agentic systems, including agent frameworks, evaluations, or tool use
- Production experience working with and modeling geospatial and Earth observation data, including tools such as GDAL, rasterio, or Earth Engine
- Experience designing, implementing, and managing security measures and controls in production
- Experience with Docker, MLflow, Kubeflow, Kubernetes or GKE, and Terraform
X, The Moonshot Factory Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about X, The Moonshot Factory and has not been reviewed or approved by X, The Moonshot Factory.
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Fair & Transparent Compensation — Pay is considered competitive for core technical and senior roles, with employer-posted ranges and clear statements that total compensation includes base, bonus, equity, and benefits. Feedback suggests posted bands and explicit structure provide clarity on how pay is constructed.
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Parental & Family Support — Family support is described as generous, including paid parental leave, baby bonding, and transitional support for parents returning to work. Fertility treatments and maternity care are also covered, indicating depth in family-focused provisions.
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Retirement Support — Retirement programs include a 401(k) with a notable company match and immediate vesting of matched funds. Additional financial supports such as student loan reimbursement and coaching strengthen long-term financial security.
X, The Moonshot Factory Insights
What We Do
We create breakthrough technologies to help solve some of the world’s biggest problems. Born at Google, we got our start creating self-driving cars and smart glasses. Since then, we’ve continued to bring sci-fi ideas into reality.
