Machine Learning Engineer

Abaka AI; Mountain View (CA); On-site; USD 175,000 - 275,000; Full time

Benefits offered by this job

Equity

Health, dental, vision, PTO, flexible

Job summary

Abaka AI in Silicon Valley is hiring a Machine Learning Engineer to shape how we build, train, and optimize multimodal AI systems. You will own scalable training pipelines and work with data engineering and research teams to drive model development across text, image, audio, video, and 3D data.

As an early member of the engineering team, you will influence model training strategy, experimentation frameworks, distributed infrastructure, and governance practices to ensure reproducibility and high

Qualifications

• 3+ years of experience in applied machine learning or ML engineering.
• Proficient in Python and ML frameworks such as PyTorch, TensorFlow or JAX.
• Experience with multimodal data processing and dataset optimization for model training.
• Knowledge of ML system design: data loaders, feature pipelines, model serving, evaluation.
• Experience with Kubernetes, Ray, Airflow or MLflow and cloud training environments (AWS, GCP, Azure).
• Strong communication and collaboration across engineering, research and product teams.

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.

Skills

Python

PyTorch

TensorFlow

JAX

Distributed training

Multimodal data

Kubernetes

Ray

Airflow

Education

Master's or PhD preferred

Tools

Kubernetes

Ray

Airflow

MLflow

AWS/GCP/Azure

Job description

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.

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).

Compensation Range: $175K - $275K