<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <p><strong>About the Role</strong></p><p>We are seeking a <strong>Senior AI Engineer</strong> to join the AI Innovation Lab. In this role, you will design, implement, and productize advanced machine learning systems to enhance engineering workflows and drive operational efficiency across Our Company.</p><p><br/></p><p>As part of the AI Lab, you will work closely with cross-functional teams to build scalable, cloud-native ML solutions that address real-world challenges in engineering and operations.</p><p><br/></p><p><strong>What You'll Do</strong></p><ul><li> Bridge business problems and ML solutions. Translate vague requirements into concrete models that actually move the needle</li><li>Own the full ML lifecycle: design, train, deploy, and iterate on production systems that handle real scale and reliability demands</li><li>Experiment rapidly with new techniques (LLMs, GenAI, novel architectures) and know which ones to kill vs. productize</li><li>Build and maintain bulletproof data pipelines and inference infrastructure that runs 24/7 without your hand-holding</li><li>Instrument your systems for the real world: monitoring drift, performance degradation, and unexpected edge cases</li><li>Push MLOps forward: reproducible training, automated CI/CD, versioning, governance. The unglamorous stuff that keeps production from catching fire</li><li>Partner with cross-functional teams (engineers, data scientists, product) to ship things that work, not just things that publish</li><li>Stay sharp on the research frontier and ruthlessly evaluate what's hype vs. what actually solves your problems</li></ul><p><br/></p><p><strong>What You Need</strong></p><ul><li>MS in CS/ML/adjacent field OR equivalent professional depth (5+ years in production ML systems)</li><li>Deep hands-on experience shipping ML at scale. You've lived through retraining failures, model drift, cold starts</li><li>Cloud-native thinking: AWS/Azure for compute, storage, training pipelines, serving infrastructure</li><li>Python fluency plus core ML stack (PyTorch, TensorFlow, scikit-learn). You can code, not just Jupyter-notebook</li><li>Real understanding of ML systems architecture: data pipelines, feature stores, model serving, experiment tracking</li><li>Practical MLOps chops: you've used MLflow, SageMaker, Kubeflow, or built your own. You know why CI/CD matters for ML</li><li>Grounded understanding of LLMs and GenAI. Prompting techniques, inference optimization, when to use vs. avoid</li><li>Strong software fundamentals: OOP, version control, testing, readability. ML engineers who write bad code are dangerous</li><li> Comfortable moving fast in ambiguous environments, working solo when needed, and shipping with others</li></ul><p><br/></p><p><strong>What We Offer </strong></p><ul><li>Flexible work environment, allowing for full-time remote work globally for positions that can be performed outside a company office or customer location</li><li>Access to employee discounts on world-class technology and partner products (leading audio and technology brands, etc.)</li><li>Extensive training opportunities through our own our professional development programs</li><li>Competitive wellness benefits</li></ul><p><br/></p><p><br/></p> </div>