Machine Learning Engineer jobs in Boston, MA

The role involves developing generalizable tools and libraries to support machine learning projects and facilitating code reuse across the team. You will collaborate with data scientists to transition models from prototype to production while maintaining high standards for code quality and documentation.

The role involves advancing AI solutions from proof of concept to full production readiness while ensuring scalability and robustness. You will write high-quality code, implement testing protocols, and collaborate with stakeholders to align technical outputs with business needs.

Define and implement agentic AI architectures and policy-as-code standards to optimize cloud financial management across multi-cloud environments. Lead delivery squads in executing FinOps automations, forecasting, and executive reporting to drive measurable business value.

You will build and scale core ML platforms and infrastructure to enable AI engineers to train and deploy models efficiently. This involves leading technical execution, conducting design reviews, and collaborating with cross-functional teams to ensure system reliability and performance.

Lead a diverse team of engineers to develop and maintain large-scale AI model and software evaluation frameworks for autonomous vehicles. Drive innovation in data warehousing and analysis while ensuring rigorous validation of ML model performance and safety.

Develop and train ML pipelines for multimodal sensor data to identify critical driving scenarios and edge cases. Build scalable data preprocessing pipelines and monitor production model health and performance.

You will design, develop, and implement VoIP infrastructure and observability features to support AI-driven voice workloads. Additionally, you will collaborate with cross-functional teams to ensure platform scalability and mentor colleagues in telecom and AI engineering disciplines.

Lead the technical direction and architecture for agentic AI systems that process global trade data for real-time decision-making. Drive the full lifecycle of ML solutions, from ingestion and model training to observability and production deployment.

Lead the technical direction and architecture for agentic systems that process global trade data to provide real-time risk and compliance decisions. Build and maintain the end-to-end pipeline from data ingestion to model inference, while mentoring senior engineers and shaping the product roadmap.