Strong academic background in computer science, artificial intelligence, machine learning, or related fields; a Master's degree or Ph.D. is preferred.
At least 3 years of experience in applied machine learning or machine learning engineering, with demonstrated ability to deliver production-ready models or pipelines.
Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or JAX, with hands-on experience in large-scale distributed training and inference systems.
Familiarity with multimodal data processing, such as text-image pairing, video understanding, and speech-audio modeling, and dataset optimization for model training.
Solid understanding of machine learning 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 such as AWS, GCP, or Azure.
Ability to work effectively across engineering, research, and product teams.
Comfort operating in a fast-paced startup environment and demonstrating ownership and urgency in execution.
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 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 to 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 integrate model outputs into internal and client-facing applications.
Support best practices for model governance, experiment tracking, and documentation to maintain engineering standards and reproducibility.
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 our 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
We’re hiring a 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 will help elevate the technical bar for our clients and partners.
If you thrive in high-ownership environments and want to shape the machine learning foundation of a fast-moving AI company, this role offers an opportunity to make an immediate and lasting impact.
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
The base salary range for this position is $175,000 - $275,000 USD annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work at Abaka AI. This role is eligible for equity, as well as a comprehensive benefits package (health, dental, vision, PTO, flexible work schedule).