Job Description
Join QuantumLeap Dynamics at the forefront of AI innovation where you'll architect cutting-edge machine learning solutions that redefine industry standards. We're seeking a visionary Senior Machine Learning Engineer to drive our next generation of autonomous systems and predictive analytics platforms. Work alongside world-class researchers in our state-of-the-art Austin R&D facility, where your expertise will directly impact breakthrough applications in healthcare, climate science, and robotics. We offer competitive equity packages, flexible hybrid work arrangements, and unparalleled opportunities to publish research in top-tier journals.
Our ideal candidate thrives in collaborative environments where science meets scalable engineering. You'll transform complex theoretical models into production-ready systems serving millions of users while mentoring junior engineers and contributing to our open-source initiatives.
Responsibilities
- Design and implement scalable ML pipelines for real-time data processing and inference
- Lead development of deep learning architectures for computer vision and NLP applications
- Optimize model performance through advanced techniques like federated learning and transfer learning
- Collaborate with cross-functional teams to integrate ML solutions into enterprise products
- Conduct rigorous A/B testing and statistical analysis to validate model efficacy
- Mentor junior engineers and contribute to technical documentation
- Stay current with emerging research through conferences and academic partnerships
Qualifications
- MS/PhD in Computer Science, Mathematics, or related quantitative field
- 5+ years of production ML experience with TensorFlow/PyTorch frameworks
- Expertise in distributed computing (Spark, Dask) and cloud deployment (AWS/GCP)
- Strong foundation in algorithms, data structures, and system design
- Proven track record of publishing in top-tier ML conferences (NeurIPS, ICML)
- Experience with MLOps tools (MLflow, Kubeflow) for CI/CD pipelines
- Demonstrable impact in deploying models at scale (>1M users)