Top AI & Machine Learning Jobs

Lead global product marketing strategy and go-to-market for EdgeCortix's AI hardware and software platforms. Shape category positioning, drive product launches, competitive intelligence, partner messaging, and cross-functional alignment while building a global product marketing team and representing the company to customers, partners, analysts, and media.

Lead and grow a kernel engineering team while designing and implementing high-performance compute kernels for specialized AI hardware, collaborating with ML research and hardware teams to optimize performance and integration.

Design and implement Python-based tools, pipelines, and automated workflows to build digital twins, soft sensors, and analytics for process optimization. Analyze large-scale time-series and historian data to improve yield, throughput, and reliability. Partner with engineers and software teams to productionize models, validate deployments, and monitor performance, driving end-to-end data-driven solutions for manufacturing processes.

Design, develop, and deploy machine learning models and AI platforms. Preprocess and analyze large datasets, implement scalable cloud-based solutions, integrate models into applications, monitor and optimize model performance, document code and processes, and collaborate with cross-functional teams to deliver AI solutions.

As a Senior Machine Learning Engineer, you will drive AI vision, develop models, and collaborate cross-functionally to enhance user experiences.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

Lead design and orchestration of AI-first agent workflows to produce application code, infrastructure, and tests. Architect full-stack systems and MCP servers, validate AI-generated outputs for correctness, security, and performance, build verification pipelines and observability, mentor teams, and evolve AI-First SDLC and governance practices.

Build and expand in-database ML model catalog: implement new model types, ensure framework parity with scikit-learn/Spark ML, optimize training and inference at petabyte scale, extend SQL-level linear algebra, and collaborate across teams to deliver correct, performant ML features and documentation.

Use large company datasets to build, validate, and deploy predictive models and algorithms to optimize product, marketing, and business outcomes. Partner with stakeholders to identify opportunities, implement models, monitor performance, and ensure data accuracy. Analyze third-party analytics and distributed data sources, create data architectures, and present insights to cross-functional teams.

Build and maintain ML training and inference infrastructure, model APIs, and design pipelines. Partner with ML and protein design scientists to convert research into reproducible tooling and production services. Optimize compute and storage, implement CI/CD, monitoring, and follow engineering best practices to scale and accelerate protein design workflows.