Senior Machine Learning Engineer

About Prove As the world moves to a mobile-first economy, businesses need to modernize how they acquire, engage with and enable consumers. Prove’s phone-centric identity tokenization and passive cryptographic authentication solutions reduce friction, enhance security and privacy across all digital channels, and accelerate revenues while reducing operating expenses and fraud losses. Over 1,000 enterprise customers use Prove’s platform to process 20 billion customer requests annually across industries, including banking, lending, healthcare, gaming, crypto, e-commerce, marketplaces, and payments. For the latest updates from Prove, follow us on LinkedIn. Prove is driving the future of digital identity. We are looking for Provers who know how to make an impact. We’re talking self-starting professionals who thrive in a fast-paced environment, process information quickly, and make intelligent decisions. The work is challenging and requires not only smart but natural curiosity and tenacity. Teamwork is also important to us – we work together and play together. Prove has big plans, and we’re excited about the future. If this sounds like the place for you – come join our team! Title: Senior Machine Learning Engineer Department: Data Intelligence Reports To: Senior Manager, Data Intelligence FLSA Status: Exempt Location: US Remote Job Summary We are looking for a Senior Machine Learning Engineer to join our team and play a critical role in bridging data science and software engineering. In this role, you will partner with data scientists to take models from prototype to production, and with software engineers to build APIs and services that are performant, reliable, and scalable. You’ll be responsible for ensuring that machine learning systems are production-ready, monitored, and continuously improved. This position is ideal for someone who thrives at the intersection of machine learning, software engineering, and MLOps. Key Responsibilities Model Deployment Integration 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. Performance Reliability 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. Engineering MLOps 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. Collaboration Leadership 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. Qualifications Education Experience 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). Master’s in Computer Science, Engineering, or related field, or equivalent experience (preferred). Technical Skills 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. Soft Skills Strong collaboration skills with both data scientists and software engineers. A ... (truncated, view full listing at source)