AI Research Engineer, Multimodal Reinforcement Learning at Tether

Role overview

The AI model team researches multimodal reinforcement learning for decision making and adaptive behaviour. This role develops training algorithms and infrastructure for models that work with text, images and audio, including constrained hardware settings. The posting lists Hanoi, Vietnam.

Responsibilities

• Research reinforcement-learning algorithms for multimodal and vision-centric models, including diffusion and autoregressive approaches.
• Build training infrastructure and reward models for large-scale experiments and improve training stability.
• Curate simulation environments and multimodal datasets, then evaluate policy performance across tasks.
• Investigate reward hacking, sample inefficiency and optimisation bottlenecks; test alternative learning strategies.

Qualifications

• A master’s degree in computer science or related field; a PhD in machine learning, NLP or vision is preferred.
• Large-scale multimodal or vision reinforcement-learning experiments with measurable policy improvements.
• Deep knowledge of RL optimisation, exploration and sample efficiency in high-dimensional settings.
• PyTorch and end-to-end simulation, training, evaluation and deployment work, with a research publication record.

Technology in this role

• PyTorch: PyTorch and end-to-end simulation, training, evaluation and deployment work, with a research publication .
• 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 (Multi-Modal Reinforcement Learning) - 100% Remote Worldwide 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: “The AI model team researches multimodal reinforcement learning for decision making and adaptive behaviour. This role develops training algorithms and infrastructure for models that work with text, images and audio,…” 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 “Research reinforcement-learning algorithms for multimodal and vision-centric models, including diffusion and autoregressive approaches.”; “Build training infrastructure and reward models for large-scale experiments and improve training stability.”; “Curate simulation environments and multimodal datasets, then evaluate policy performance across tasks.”. 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 “A master’s degree in computer science or related field; a PhD in machine learning, NLP or vision is preferred.”; “Large-scale multimodal or vision reinforcement-learning experiments with measurable policy improvements.”; “Deep knowledge of RL optimisation, exploration and sample efficiency in high-dimensional settings.”. 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. Tailor the first half of your CV to the named work instead of using a generic summary. Lead with two or three outcomes that match the responsibilities, and include enough context to show complexity, scope and your role. Add technical detail where it substantiates an outcome: architecture, interfaces, data or traffic scale, test strategy, deployment approach, incident response or collaboration.

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.

Related jobs

• AI Principal Machine Learning Engineer (10189) at Extreme Networks — Extreme Networks
• Senior Computer Vision Data Scientist at Endava — Endava
• Senior Machine Learning Engineer at Kake — Kake
• AI Engineer at Bosch Global Software Technologies — Bosch
• Staff Machine Learning Engineer, Ads Efficiency at Reddit — Reddit
• Lead AI Engineer — Bosch