Top Hybrid Machine Learning Engineer Jobs

The Applied Scientist will drive research on efficient adaptive machine learning, deploy ML systems end-to-end, and contribute to product strategy, focusing on real-world impact and innovation.

Develop and optimize AI models and training systems for robotics applications, focusing on design, implementation, and performance optimization of ML models.

The role involves developing scalable machine learning applications, collaborating across teams, and enhancing data products at Poshmark.

This role involves building and scaling machine learning systems for content understanding, improving quality, and collaborating with cross-functional teams to enhance user experience on Spotify.

Hands-on role to design, prototype, and ship production AI agents, copilots, and automation workflows. Integrate AI tooling with enterprise data platforms and OT systems, own technical delivery end-to-end, build monitoring and guardrails, troubleshoot scaling issues, and create reusable accelerators while mentoring junior talent.

Lead design and build of an artifact-first knowledge platform: ingestion, extraction, retrieval, provenance, approval, and review UIs. Ship reliable, observable production systems that scale across firms, mentor engineers, partner with tax professionals, and drive product-quality and correctness for applied-AI workflows.

Build and maintain production-quality Python code for portfolio construction, optimization, and statistical analysis. Automate investment workflows, validate models, and create interactive visualizations to support investment decision-making and reporting.

Build and deploy ML models and pipelines for entity resolution and a knowledge graph at scale. Fine-tune algorithms, expand data ingestion and enrichment, create evaluation harnesses, and integrate agentic workflows to support research and product teams processing tens of millions of records daily.

Lead product strategy and execution for AI-driven expert research: translate investor needs into AI workflows, define roadmap, run user research and experiments, collaborate with engineering/design, and drive launches while optimizing accuracy and reliability of LLM/ML outputs.

Lead and scale IAA's data engineering and data science engineering function: define vision and architecture for Azure-based data/BI/ML platforms, drive Microsoft Fabric/Synapse/Power BI implementations, partner with business stakeholders to deliver analytics, forecasting, and ML solutions, mentor and grow teams, and ensure delivery quality, governance, and operational excellence.