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greenhouse

Machine Learning Engineer

Abaka AI
Palo Alto, USA
Full-time
Mid · 3+ years experience
Onsite
USD 175000-275000 yearly / year
Discovered 1 months ago
PythonPyTorchTensorFlowJAXKubernetesRay

Full Job Posting

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.

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