Machine Learning Engineer

Responsibilities

• Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data.
• Work closely with data engineering and research teams to develop efficient data workflows, including collection, preprocessing, annotation, versioning, and model integration.
• Implement and refine training strategies for large-scale AI systems, including vision, video, and diffusion models, ensuring reproducibility, efficiency, and strong model performance.
• Develop tools and automation frameworks that accelerate model experimentation, hyperparameter tuning, and deployment.
• Identify and address performance bottlenecks in data or training pipelines to improve throughput, stability, and resource utilization.
• Collaborate with product and infrastructure teams to ensure smooth integration of model outputs into both internal and client-facing applications.
• Support internal best practices for model governance, experiment tracking, and documentation to maintain high engineering standards and reproducibility.

Qualifications

• Strong academic background in computer science, artificial intelligence, machine learning, or related fields. Master’s degree or Ph.D. is preferred.
• 3+ years of experience in applied machine learning or ML engineering, with a demonstrated ability to deliver production-ready models or pipelines.
• Proficient in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, with hands-on experience in large-scale distributed training and inference systems.
• Familiarity with multimodal data processing (e.g., text-image pairing, video understanding, speech-audio modeling) and dataset optimization for model training.
• Solid understanding of ML system design, including feature pipelines, data loaders, model serving, and evaluation frameworks.
• Experience with modern infrastructure tools such as Kubernetes, Ray, Airflow, or MLflow, along with cloud-based training environments (AWS, GCP, Azure).
• Excellent communication and collaboration skills, capable of working effectively across engineering, research, and product teams to accomplish shared goals.
• Self-driven and adaptable, comfortable operating in a fast-paced startup environment, and able to demonstrate strong ownership and urgency in execution.

Compensation & Benefits