We re looking for a seasoned
Staff MLE
to shape and scale the backbone of our production ML ecosystem. In this role, you will architect high-performing ML systems that power our geospatial intelligence platform, transforming large-scale ellite and aerial imagery into actionable insights. You ll lead end-to-end ownership from model deployment and MLOps to infrastructure design while partnering closely with data science, platform engineering, and product teams to deliver reliable, scalable, and cost-efficient ML solutions. If you thrive at the intersection of deep technical expertise, system design, and cross-functional collaboration, this role is for you. How youll make an impact:
ML System Architecture & Production Deployment
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Design, build, and maintain end-to-end ML pipelines for batch processing of ellite and aerial imagery
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Deploy and scale ML models in production on AWS infrastructure, leveraging services like SageMaker, Bedrock,or custom-built solutions
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Implement MLflow for experiment tracking, model versioning, and model registry management
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Architect batch inference systems optimized for throughput and cost-efficiency
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Work with geospatial data formats and coordinate reference systems
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Collaborate with data scientists to transition models from research to production
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Partner with platform engineering to build scalable compute, GPU clusters, and storage layers
ML Operations & Reliability
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Implement comprehensive model itoring systems to track performance degradation and data drift
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Design and execute canary deployments and A/B testing frameworks for safe model rollouts
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Build active learning pipelines to continuously improve model performance with minimal labeling effort
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Establish model evaluation frameworks and performance bench king processes
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Create alerting and observability systems for production ML workloads
Technical Leadership
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Mentor ML engineers and data scientists on best practices for production ML
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Drive technical ision-making on ML infrastructure and tooling
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Collaborate with platform and data engineering teams to optimize the ML stack
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Establish engineering standards and contribute to architectural roadmaps
What we re looking for:
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5+ years of experience in machine learning engineering with 2+ years in a senior or lead capacity
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Proven track record deploying and maintaining ML systems in production using AWS services (SageMaker,Lambda, ECS/EKS, S3, etc.)
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Strong hands-on experience with tools like MLflow, WandB, or similar for experiment tracking and model management
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Deep expertise in image segmentation and computer vision techniques using frameworks like PyTorch or TensorFlow
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Production experience with ensemble models (xgboost, lightgbm, RF)
ML Operations Expertise
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Experience implementing model itoring, drift detection, and alerting systems
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Hands-on experience with canary deployments, A/B testing and Shadow deployments for ML models
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Knowledge of active learning strategies and human-in-the-loop ML systems
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Strong understanding of model evaluation metrics, bias detection, and performance analysis
Technical Skills
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Expert-level Python programming with ML libraries (scikit-learn, PyTorch/TensorFlow, NumPy, pandas, etc)
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Experience with distributed batch processing frameworks (Airflow, Step Functions, Argo Workflows, Spark,Dask, Ray or similar)
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Proficiency with AWS ML ecosystem and infrastructure-as-code (Terraform, CloudFormation)
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Hands-on experience with dataset versioning tools such as DVC, LakeFS, Delta Lake, Quilt, or Pachyderm
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Strong software engineering fundamentals: unit/integration testing, CI/CD, version control, observability, designpatterns
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Experience with containerization (Docker, Kubernetes) for model deployment
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Good to have experience with ML Orchestration tools like Kubeflow, Vertex AI, etc
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Nice to have experience with GPUs: scheduling GPU jobs, optimizing GPU performance, memory profiling
We are proud to be an equal-opportunity employer. We are committed to embracing diversity and inclusion in our hiring practices, and we promote a work environment where everyone, from any race, color, religion, sex, sexual orientation, gender identity, or national origin, can do their best work.
We are committed to providing an inclusive and accessible interview experience for all candi . Please let us know if you require any accommodation during the interview process, and we will make every effort to meet your needs.
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