Machine Learning Engineer, Bellwether, X (AI)

Location: Mountain View, California, United States

Base salary: $174,000–$261,000 per year, plus bonus, equity, and benefits.

Company

hirify.global, builds and launches ambitious technologies intended to address large-scale global problems, combining research-level risk with startup speed.

What you will do

• Build and deploy machine learning applications across the full lifecycle, from prototyping through production.
• Architect ML models, pipelines, and production-grade agentic systems with observability and explainability.
• Develop and maintain Google Cloud Platform infrastructure for software development and high-volume production systems.
• Drive architecture decisions balancing business needs, rapid prototyping, and long-term system longevity.
• Debug, test, monitor, deploy, and maintain live applications and ML systems.
• Present findings to internal and external stakeholders and contribute to strategic product direction.

Requirements

• BS/MS in Computer Science or equivalent practical experience.
• 5+ years of experience across the ML development pipeline, including experimentation, development, MLOps, and scaling ML applications into robust production environments.
• Strong experience with ML frameworks such as PyTorch, TensorFlow, or JAX, and Python libraries including NumPy, SciPy, or Pandas.
• Strong Python skills and experience evolving prototypes into production systems in an early-stage environment.
• Experience with Git, Apache Beam/Dataflow, Google Compute Engine, and Vertex AI.
• Hands-on experience troubleshooting, debugging, designing code-testing processes, and monitoring deployed application performance and health.
• Deep MLOps experience including distributed training, automated evaluation, model retraining and monitoring, drift detection, data lineage, high throughput, and dynamic batching.
• Ability to work in an Agile team, contribute ideas and standards, and move between strategic thinking and detailed execution.

Nice to have

• Experience building LLM-based or agentic systems, including agent frameworks, evaluations, and tool use.
• Production experience with geospatial and Earth observation data and tools such as GDAL, rasterio, or Earth Engine.
• Experience designing, implementing, and managing production security measures and controls.
• Experience with Docker, MLflow, Kubeflow, Kubernetes/GKE, or Terraform.

Culture & Benefits

• Full-time individual contributor role focused on real-world products rather than pure research.
• Small, highly collaborative team using pair programming and Agile sprints.
• Work spans early-stage development and production delivery in a fast-paced, fluid environment.
• Compensation includes base salary, bonus, equity, and benefits; individual pay depends on work location, skills, experience, and education or training.
• Equal opportunity workplace with accommodation support available for disabilities or special needs.