About Pebble Falcon Falcon is a platform built by Pebble to help enterprises reduce compute waste, optimize infrastructure efficiency, and minimize energy and carbon costs across hybrid cloud environments.
Our AI-powered insights help businesses make smarter decisions about when and how their workloads run — driving major savings and sustainability.
Key Responsibilities : Analyze real-time cloud emissions/energy data collected from Falcon and generate actionable insights.
Develop models to forecast emissions trends based on historical workload data and AI-driven anomaly detection.
Profile AI workloads (training & inference models) to determine energy footprint per model, GPU utilization efficiency, and energy cost per inference run.
Build carbon-aware workload scheduling models that optimize AI jobs across different cloud regions based on renewable energy availability.
Develop AI-powered recommendations for reducing cloud emissions and optimizing workloads (e.g., instance right-sizing, model quantization, auto-scaling improvements).
Leverage Open Telemetry, e BPF, or custom profiling tools to extract deep insights into how AI models consume compute, memory, and GPU cycles.
Collaborate with data engineering teams to build scalable pipelines for emissions analytics using Spark, Flink, or Dask.
Ensure model explainability by developing interpretable AI-driven insights for cloud sustainability reporting.
Required Skills and Qualifications : 5+ years of experience in AI/ML engineering, data science, or cloud infrastructure analytics.
Expertise in AI model profiling, deep learning efficiency, and cloud workload optimization.
Strong knowledge of Py Torch, Tensor Flow, and ONNX for AI workload analysis.
Experience with GPU profiling tools (NVIDIA Nsight, Tensor RT, Triton Inference Server, Deep Seek).
Strong programming skills in Python, Go, or Rust for building ML pipelines and cloud integrations.
Experience with time-series forecasting, anomaly detection, and predictive modeling.
Familiarity with AWS/GCP/Azure cloud APIs to extract resource utilization data for AI workloads.
Experience with MLOps pipelines and automated model monitoring (Kubeflow, MLflow, Sage Maker).
Understanding of carbon-aware computing , energy-efficient AI, and green computing strategies.
Bonus or Good-to-Have Skills : Experience with LLM model efficiency (e.g., pruning, quantization, distillation).
Knowledge of sustainability reporting frameworks (GHG, SASB, CSRD).
Familiarity with Green AI research and techniques for reducing model energy consumption.
Experience with serverless AI inference to optimize cloud energy usage.
Why Join Us? Tackle one of the biggest challenges in AI—reducing the carbon impact of machine learning models.
Build an AI-powered sustainability observability platform for cloud.
Work at the intersection of AI, cloud infrastructure, and sustainability.
Join a fast-moving startup with the ambition to change how enterprises approach cloud sustainability.
