AI Research Engineer, Multimodal Vision at Tether

Role overview

This AI model role covers the full vision-language development cycle, from curated datasets and training pipelines to evaluation and deployment efficiency. The employer seeks measurable improvements for real-world and resource-limited use. Each requisition states its own remote location.

Responsibilities

• Research, train, evaluate and optimise vision-language models.
• Build post-training pipelines using supervised fine-tuning, knowledge distillation and reinforcement learning from feedback.
• Curate, filter and balance multimodal datasets for domain-specific tasks.
• Compress and optimise models for constrained hardware; define benchmarks for quality, robustness and real-world performance.
• Scale training on distributed GPUs, resolve pipeline bottlenecks and contribute relevant open-source research and tools.

Qualifications

• Computer science, machine learning or related degree; a master’s or PhD is preferred.
• Experience with multimodal post-training, parameter-efficient fine-tuning and distributed training.
• Demonstrated improvements to vision-language models on benchmarks or real-world tasks.
• Experience adapting models to constrained environments, plus open-source contributions and AI research publications as listed by the employer.

Technology in this role

• CAN: We expect you to bring a research-driven mindset combined with strong engineering discipline - someone who can identify the right technique for a given problem, implement it rigorously, and measure its impact .

Role-specific candidate guide

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

Understanding the scope. This AI Research Engineer (Multi-Modal & Vision) 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: “This AI model role covers the full vision-language development cycle, from curated datasets and training pipelines to evaluation and deployment efficiency. The employer seeks measurable improvements for real-world and…” 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, train, evaluate and optimise vision-language models.”; “Build post-training pipelines using supervised fine-tuning, knowledge distillation and reinforcement learning from feedback.”; “Curate, filter and balance multimodal datasets for domain-specific 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 “Computer science, machine learning or related degree; a master’s or PhD is preferred.”; “Experience with multimodal post-training, parameter-efficient fine-tuning and distributed training.”; “Demonstrated improvements to vision-language models on benchmarks or real-world tasks.”. 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 CAN: We expect you to bring a research-driven mindset combined with strong engineering discipline - someone who can identify the right technique for a given problem, implement it rigorously, and measure its impact .. 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.

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 Enablement Engineer — Keyloop
• Embedded Linux Consultant Engineer (Remote/Anywhere) at Collabora — Collabora
• Embedded System Test Engineer, Home Appliances at Bosch — Bosch
• Enterprise Support Engineer at BlueCat Networks — BlueCat Networks
• AI Product Builder — Money Forward Vietnam
• Java Product Support Engineer at Sonatype — Sonatype