Machine Learning Researcher, Multimodal LLMs
Location: San Francisco, CA or Remotes
About Bland
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.
The Role
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
Strong LLM / Multimodal Background
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
Fast Experimental Loop
You can go from idea → dataset → experiment → conclusion in days
You know how to design experiments that actually answer the question
Product Intuition
Strong sense for what makes an interaction feel natural vs robotic
Ability to translate abstract modeling ideas into user-facing improvements
Builder Mentality
You take ownership from research through deployment
You thrive in ambiguous, fast-moving environments
You care about impact, not just elegance
How You Show Up
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
Bonus Points
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
Compensation & Benefits
Competitive salary: $180,000 – $260,000
Meaningful equity
Full healthcare, dental, vision
Office in Jackson Square, SF
High autonomy, high impact