Senior AI​/ML Engineer

About Poseidon

Poseidon is building the data infrastructure the next generation of AI will depend on. Foundation models are not limited by compute. They are bottlenecked by rare, high-quality, IP-safe data that actually improves performance: the long-tail, edge-case, multi-modal datasets that cannot be scraped or synthetically generated.

We are creating a decentralized data layer connecting AI companies with the datasets they need. Poseidon is the infrastructure that makes scalable, compliant, demand-driven data sourcing possible. Backed by a16z, we are early, moving fast, and looking for mission-driven teammates to shape this category.

Examples of the datasets we work with include conversational audio, complex video, domain-specific imagery, and other real-world data that enables models to perform reliably outside controlled environments.

As one of the first engineers on the platform team, you will help build the production systems that power this platform: the data pipelines, model workflows, and infrastructure that transform raw multimodal data into production-ready training datasets and AI capabilities.

We are hiring a Senior AI / ML Engineer focused on production systems to build the core platform that powers Poseidon’s machine learning workflows to deliver results to customers.

You will work closely with research engineers who experiment with models and tuning approaches. Your responsibility is to build the infrastructure and pipelines that allow those experiments to run reliably and repeatedly in scaled production.

This role sits at the intersection of machine learning engineering, data infrastructure, and distributed systems. The goal is to build production pipelines that allow Poseidon to process multimodal datasets, fine-tune models, and deliver results to customers quickly.

• Build scalable pipelines for multimodal data processing and model training
• Productionize research workflows so models and training processes can run reliably in production
• Design infrastructure that supports data ingestion, annotation, labelling, dataset generation, and model fine-tuning
• Build internal services and tooling used by researchers to run training and evaluation workflows
• Optimize GPU and compute utilization across training pipelines
• Implement monitoring, logging, and observability across ML systems
• Ensure pipelines are reproducible, performant, and production ready

• ~3-5+ years building production machine learning systems
• Bachelor or Master (preferred) in Computer Science, Engineering, Math, or related area.
• Strong Python engineering experience
• Experience building scalable data pipelines or ML infrastructure
• Experience working with modern ML frameworks such as PyTorch, Ray, Ne Mo
• Familiarity with GPU compute and CUDA concepts
• Experience deploying ML systems in production environments
• Experience working with cloud infrastructure (AWS preferred) and infrastructure platforms such as Prime Intellect is a plus.
• Familiarity with containerization and deployment patterns (Docker or similar)

You should be comfortable building systems that support training pipelines, distributed data processing, model evaluation workflows, and production inference.

Experience with distributed compute frameworks, ML workflow orchestration, or large‑scale dataset processing is a plus.

You will help build the infrastructure that powers how modern AI systems are trained and deployed. The systems you build will enable researchers to iterate quickly, process multimodal data at scale, and deliver production AI systems to real customers.

As an early engineer, you will play a foundational role in shaping the architecture, tooling, and technical direction of the platform. We are an early stage AI startup, you will have the opportunity to make a huge impact on the company and the industry.