Machine Learning Researcher, Multimodal LLMs

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Наша компания

At Bland.com our mission is to empower enterprises to build AI phone agents at scale. Voice is quickly becoming the primary interface between businesses and their customers, and we are building the models and infrastructure that make those interactions feel natural, reliable, and genuinely human. We’ve raised $65M from leading investors including Emergence Capital, Scale Venture Partners, Y Combinator, and founders of Twilio, Affirm, and ElevenLabs.

Чем предстоит заниматься

We are looking for someone to contribute to the development of our next-generation multimodal LLM stack, combining speech, text, tools, and real-time reasoning into a single unified system. You’ll be responsible for building industry-leading conversational AI models that power Bland's agent, and taking them all the way from idea to production

At Bland, we're not just thinking about text modeling. You will define how our agents listen, think, and act in real time, integrating streaming audio, tool execution, and dynamic context into a single coherent system. You will take ideas from research through production systems serving millions of calls per day

WHAT MAKES YOU A GREAT FIT

Наши требования

Experience with real-time voice systems or conversational AI

Background in tool-using agents or agent frameworks

Experience with multimodal datasets (audio + text + actions)

Contributions to LLM or speech-related research or open source

Experience with LLMs, multimodal models, or speech-language systems

Deep understanding of prompting, fine-tuning, and alignment techniques

Familiarity with neural audio codecs and modern multimodal LLM techniques

Мы предлагаем

Competitive salary: $180,000 – $260,000

Meaningful equity

Full healthcare, dental, vision

Office in Jackson Square, SF

High autonomy, high impact

Дополнительно

You can go from idea → dataset → experiment → conclusion in days

You know how to design experiments that actually answer the question

Strong sense for what makes an interaction feel natural vs robotic

Ability to translate abstract modeling ideas into user-facing improvements

You take ownership from research through deployment

You thrive in ambiguous, fast-moving environments

You care about impact, not just elegance

You think in systems, not just models

You obsess over latency, correctness, and real-world behavior

You are comfortable discarding ideas quickly when data disagrees

You push toward simple abstractions for complex problems

Bland AI

San Francisco

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Опубликовано21 апр.

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