AI Research Engineer, LLM and Multimodal Pretraining at Tether

Role overview

Tether’s AI model team seeks a researcher to improve architectures and pretraining for language and multimodal models. Work spans text, vision and audio data, with large distributed training runs and attention to efficiency and cross-modal alignment. Each requisition specifies its own remote location.

Responsibilities

• Run foundational LLM and multimodal pretraining across distributed, multi-node NVIDIA GPU systems.
• Prototype and scale architectures, tokenizers and cross-modal alignment methods.
• Source, filter and curate large text and multimodal datasets and build reliable training data pipelines.
• Design experiments, analyse results and improve performance and token efficiency.
• Debug long-running training bottlenecks and improve the scalability and hardware efficiency of distributed systems.

Qualifications

• Computer science or related degree; a PhD in NLP or machine learning and research publications are preferred.
• Hands-on contribution to large-scale LLM or multimodal pretraining on distributed GPU infrastructure.
• Practical familiarity with distributed training frameworks and model architecture changes that improve intelligence, efficiency or scalability.
• Strong PyTorch and Hugging Face experience in model development, continued pretraining and deployment.

Technology in this role

• PyTorch: Strong PyTorch and Hugging Face experience in model development, continued pretraining and .
• R: Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* .
• LLM: Run foundational LLM and multimodal pretraining across distributed, multi-node NVIDIA GPU .
• CAN: If you’re unsure, you can confirm their identity by checking their profile or contacting us through our .

Role-specific candidate guide

Preparation notes based on the employer’s stated responsibilities and qualifications.

Understanding the scope. This AI Research Engineer (Pre-training - LLM & Multi-Modal) position at Tether is best assessed through the work described in the employer’s posting, rather than the title alone. The available role overview says: “Tether’s AI model team seeks a researcher to improve architectures and pretraining for language and multimodal models. Work spans text, vision and audio data, with large distributed training runs and attention to…” Read that statement together with the responsibilities below to understand the likely balance between delivery, coordination and technical decision-making. The role belongs to the AI and data area, but that label is only a guide to the work; the employer’s own requirements remain decisive. Before applying, check the original listing for reporting lines, product or team assignment, employment terms and any details that may have changed since this copy was checked. Where this page says a fact is not specified, treat it as an open question rather than an implied promise.

What the responsibilities signal. The posting specifically names “Run foundational LLM and multimodal pretraining across distributed, multi-node NVIDIA GPU systems.”; “Prototype and scale architectures, tokenizers and cross-modal alignment methods.”; “Source, filter and curate large text and multimodal datasets and build reliable training data pipelines.”. For each duty, identify a project where you performed comparable work and be ready to describe the context, your own contribution, constraints, decisions, and measurable result. Explain how you moved from the initial need to implementation, review, release or operational follow-through, where those stages apply. If the work involved other teams or users, distinguish your decisions from the group’s contribution. Avoid presenting a technology keyword as proof by itself: connect it to the problem you solved, the scale or complexity involved, and what changed after your work. The source may contain additional duties; review it directly before preparing your application.

How to read the requirements. The listed qualifications include “Computer science or related degree; a PhD in NLP or machine learning and research publications are preferred.”; “Hands-on contribution to large-scale LLM or multimodal pretraining on distributed GPU infrastructure.”; “Practical familiarity with distributed training frameworks and model architecture changes that improve intelligence, efficiency or scalability.”. Mark each requirement as demonstrated, adjacent experience or a genuine gap, then use a concise example for the first two categories. A strong example explains what you personally owned, how you made trade-offs, how you validated the result and what you learned. If the posting specifies years of experience, language ability, certifications, location or eligibility, address those facts plainly in your CV or application rather than relying on the employer to infer them. Do not claim a qualification from exposure alone. For a requirement you do not meet, describe relevant transferable experience accurately and ask the recruiter whether it is mandatory.

Technology and working evidence. The technology terms associated with this listing are PyTorch. Treat this as a set of posting-derived signals, not a guarantee that every tool is used daily or that the list is complete. For each term relevant to your experience, prepare one concrete example: the system or workflow, the part you owned, the alternatives considered, and how you tested reliability, security, performance or usability as appropriate. If you have not used a named tool, explain the closest related system you have worked with and how you would ramp up; do not relabel that experience as direct production use. If the source describes technologies beyond this list, prioritize its exact wording.

Evidence to put in the application.

About Tether

Tether develops digital-token products, including USDT, alongside projects in peer-to-peer communications and AI. The employer describes a globally distributed remote team.

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