Machine Learning Researcher, Multimodal LLMs
At a glance
Mid-level AI Research role at Bland. San Francisco · full-time · $160,000–$280,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build multimodal language models that combine speech, text, tool use and real-time reasoning
- Carry research ideas through implementation into production systems handling calls at scale
- Shape how agents process streaming audio, use tools and adapt to changing context
- Design experiments, move from data to results quickly, and use findings to guide model decisions
What you bring
- Hands-on work with large language models, multimodal systems or speech-language models
- A strong grasp of prompting, fine-tuning and alignment methods
- Familiarity with neural audio codecs and current multimodal language-model approaches
Who this fits
This mid-level role suits a researcher who can connect modeling decisions to natural user interactions and take work through deployment. The position is based in San Francisco or can be remote.
From the employer
Machine Learning Researcher, Multimodal LLMs
Location: San Francisco, CA or Remote
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 $100M 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: $160,000 – $280,000
Meaningful equity
Full healthcare, dental, vision
Office in Levi's Plaza, SF
High autonomy, high impact
Additional Information
Bland is an equal opportunity employer. We are committed to proving equal employment opportunities to all qualified applicants and employees and do not discriminate based on any legally protected characteristic.
Bland participates in the E-Verify employment verification program. All new hires are required to complete the Form I-9, and employment will be verified though E-Verify as part of the onboarding process.