AI Research Engineer

Job type:
Full Time
· Department:
Engineering
· Work type:
Hybrid

Poseidon is an a16z backed startup building a platform that coordinates supply and demand for specialized AI training data. We work with Fortune 500 enterprises and leading AI labs to coordinate large-scale, rights-cleared multi-modal training datasets.

We are seeking a full-time AI Research Engineer to join our research team. This role targets exceptional candidates from top universities (Stanford, Berkeley, MIT, CMU, etc.) who want to conduct cutting‑edge research while building production systems at the intersection of machine learning, cryptography, and data systems.

• Conduct applied research at a top a16z-funded company with contracts across F500 firms and fast-growing AI startups.
• Access large-scale multi-modal datasets (audio, video, text) to test research ideas at production scale.
• Publish at premier ML venues (ICML, NeurIPS, ICLR, CVPR) and co-organize workshops with academic and industry partners.
• Build production pipelines that deploy your research to real-world applications.
• Receive direct mentorship from Prof. Sandeep Chinchali (UT Austin, Stanford CS PhD), whose lab alumni have joined Meta, Nvidia, Google, Samsung, and leading AI startups.
• Establish your own independent AI thought leadership through publications, talks, and industry collaboration.

• Multi-modal search: combining generative AI with formal programming languages to search petabyte-scale datasets.
• Voice foundation models: training speech systems for underrepresented languages including Indian regional languages and Korean.
• Data valuation: developing fair attribution and pricing mechanisms for individual data points in ML models.
• AI cryptography: embedding watermarks in sensory data to ensure provenance, detect deepfakes, and secure AI training workflows.

• Publications in top ML conferences (NeurIPS, ICML, ICLR, CVPR).
• Expert-level proficiency in PyTorch/Tensor Flow and GPU computing.
• Strong Python programming and technical writing skills.
• MS or PhD in Computer Science, Software Engineering, or ECE with substantial deep learning experience.
• Familiarity with LLMs, audio foundation models, and vision-language models preferred.