Design and optimize scalable multimodal machine learning pipelines for training, fine-tuning, evaluation, experimentation, and deployment. Collaborate with data engineering, research, product, and infrastructure teams across text, image, audio, video, and 3D modalities. Improve distributed training performance, data workflows, reproducibility, model governance, and resource utilization while shaping technical strategy and engineering best practices.
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 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 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.
- 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.
- 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.
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).
Similar Jobs
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Designs, builds, and operates production-grade agentic AI systems at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, hybrid search, model integration, evaluation, observability, safety guardrails, and governance. The role provides architectural leadership, mentors engineers, leads design reviews, and develops scalable AI infrastructure across hyperscalers and company platforms.
Top Skills:
AnthropicC++CmdbGoGoogle AiHybrid SearchJavaKnowledge GraphMcpMlopsOpenaiPythonRagVector StoresWorkflow Data Fabric
Cloud • Information Technology • Productivity • Security • Software • App development • Automation
Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.
Top Skills:
Ai Systems InfrastructureArtificial IntelligenceAutomated EvaluationExperimentationMachine LearningMultimodal AiPrompt EngineeringRankingRetrieval
.jpg)
Marketing Tech • Mobile • Software
Own and evolve Braze’s ML platform for production-scale training, deployment, serving, observability, reliability, and cost efficiency. Lead complex infrastructure initiatives, including multi-region model serving, customer-specific model pipelines, CI/CD tooling, orchestration, and cloud identity. Set technical direction, manage incidents, collaborate across teams, improve engineering quality, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.
Top Skills:
CeleryCi/CdCloud InfrastructureFeature StoresIamInfrastructure As CodeKafkaKubernetesMl ObservabilityMlflowMongoDBNetworkingPythonRabbitMQRayRedisRuby On Rails
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
