Key skills
Machine Learning EngineeringAPI DevelopmentContainerizationCloud PlatformsPythonCI/CD PipelinesData PipelinesCollaborationCommunicationProblem-solvingJavaGoMachine LearningMLPyTorchTensorFlowscikit-learnMLOpsMLflowKubeflowAirflowFastAPIFlaskgRPCAWSKubernetesDockerSageMakerPerformance OptimizationCI/CD
About this role
Prove is a company focused on mobile-first identity solutions, and they are seeking a Senior Machine Learning Engineer to bridge data science and software engineering. The role involves collaborating with data scientists and software engineers to deploy machine learning models and ensure their performance and reliability in production.
Responsibilities:
- Collaborate with data scientists to transform research models into production-ready services
- Design, build, and maintain APIs and microservices to serve ML models at scale
- Ensure smooth integration of ML solutions into core products and platforms
- Optimize inference performance, latency, and scalability
- Build monitoring, logging, and alerting systems to track model performance and data drift
- Implement retraining pipelines and automated workflows for continuous improvement
- Develop CI/CD pipelines for machine learning workflows
- Use modern DevOps/MLOps practices to ensure reproducibility and reliability
- Partner with software engineers to design robust data and compute infrastructure
- Work closely with cross-functional teams to align model capabilities with business needs
- Mentor junior engineers and contribute to best practices in ML engineering
- Advocate for scalable, sustainable ML deployment strategies across the organization
Requirements:
- 5-7+ years of professional experience in ML engineering, software engineering, or related roles
- Bachelor's Master's in Computer Science, Engineering, or related field (required)
- Strong programming skills in Python (and experience with libraries such as scikit-learn, TensorFlow, or PyTorch), Java, and/or Go
- Hands-on experience building APIs and services (e.g., FastAPI, Flask, gRPC)
- Expertise with containerization & orchestration (Docker, Kubernetes)
- Familiarity with CI/CD pipelines and modern MLOps tools (MLflow, Airflow, Kubeflow, SageMaker, or similar)
- Strong understanding of cloud platforms (AWS)
- Solid background in data pipelines, distributed systems, and performance optimization
- Strong collaboration skills with both data scientists and software engineers
- Ability to balance technical rigor with pragmatic delivery
- Excellent communication and problem-solving abilities
- Master's in Computer Science, Engineering, or related field, or equivalent experience