Staff Machine Learning Engineer

Were 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 satellite and aerial imagery into actionable insights. Youll 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.

Responsibilities

• Design, build, and maintain end-to-end ML pipelines for batch processing of satellite and aerial imagery.
• Deploy and scale ML models in production on AWS infrastructure, leveraging services like SageMaker, Bedrock, or custom-built solutions.
• Implement MLflow for experiment tracking, model versioning, and model registry management.
• Architect batch inference systems optimised for throughput and cost-efficiency.
• Work with geospatial data formats and coordinate reference systems.
• Collaborate with data scientists to transition models from research to production.
• Partner with platform engineering to build scalable compute, GPU clusters, and storage layers.
• Implement comprehensive model monitoring systems to track performance degradation and data drift.
• Design and execute canary deployments and A/B testing frameworks for safe model rollouts.
• Build active learning pipelines to continuously improve model performance with minimal labelling effort.
• Establish model evaluation frameworks and performance benchmarking processes.
• Create alerting and observability systems for production ML workloads.
• Mentor ML engineers and data scientists on best practices for production ML.
• Drive technical decision-making on ML infrastructure and tooling.
• Collaborate with platform and data engineering teams to optimise the ML stack.
• Establish engineering standards and contribute to architectural roadmaps.
  
  

• 5+ years of experience in machine learning engineering with 2+ years in a senior or lead capacity
• Proven track record deploying and maintaining ML systems in production using AWS services (SageMaker, Lambda, ECS/EKS, S3 etc. )
• Strong hands-on experience with tools like MLflow, WandB, or similar for experiment tracking and model management.
• Deep expertise in image segmentation and computer vision techniques using frameworks like PyTorch or TensorFlow.
• Production experience with ensemble models (xgboost, lightgbm, RF).
• Experience implementing model monitoring, drift detection, and alerting systems.
• Hands-on experience with canary deployments, A/B testing and Shadow deployments for ML models.
• Knowledge of active learning strategies and human-in-the-loop ML systems.
• Strong understanding of model evaluation metrics, bias detection, and performance analysis.
• Expert-level Python programming with ML libraries (scikit-learn, PyTorch/TensorFlow, NumPy, pandas, etc).
• Experience with distributed batch processing frameworks (Airflow, Step Functions, Argo Workflows, Spark, Dask, Ray or similar).
• Proficiency with AWS ML ecosystem and infrastructure-as-code (Terraform, CloudFormation).
• Hands-on experience with dataset versioning tools such as DVC, LakeFS, Delta Lake, Quilt, or Pachyderm.
• Strong software engineering fundamentals: unit/integration testing, CI/CD, version control, observability, design patterns.
• Experience with containerization (Docker, Kubernetes) for model deployment.
• Good to have experience with ML Orchestration tools like Kubeflow, Vertex AI, etc.
• Nice to have experience with GPUs: scheduling GPU jobs, optimising GPU performance, and memory profiling.