Machine Learning Engineer
Abakaai · Mountain View, CA
About this role
**About Abaka AI** Abaka AI is built on one mission: to be the world’s most trusted data partner for AI companies. More than 1,000 industry leaders across Generative AI, Embodied AI, and Automotive AI rely on us to power their data pipelines. With headquarters in Silicon Valley and teams in Paris, Singapore, and Tokyo, we support global partners with fast, reliable, and scalable data solutions. Our offerings include a diverse catalog of off-the-shelf datasets (image, video, multimodal, reasoning, 3D, and beyond) as well as comprehensive data collection and annotation services. Whether teams need raw data, curated datasets, or full-cycle data engineering, Abaka AI provides the foundation for building high-performance AI systems. **About the Role (Machine Learning Engineer)** We’re hiring our first Machine Learning Engineer in the United States—a foundational role that will shape how Abaka builds, trains, and optimizes multimodal AI systems. You will own the design and development of scalable training pipelines, work directly with our data engineering and research teams, and help drive the technical roadmap for model development across multiple modalities. As an early member of the engineering team, you will influence core decisions around model training strategy, experimentation frameworks, distributed infrastructure, and internal best practices. Your work will directly impact the performance of frontier models trained on Abaka datasets and help elevate the technical bar for our clients and partners. **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 (e.g., vision, video, 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 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, AI, machine learning, or related fields (Master’s or Ph.D. 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. - Familiarity with multimodal data processing (e.g., text-image pairing, video understanding, speech-audio modeling) and dataset optimization for 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, and cloud training environments (AWS, GCP, Azur
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