• Build ML pipelines and backend systems • Deploy models to production • Optimize performance and latency • Develop data pipelines • Ensure scalability and reliability • Productionize models alongside the AI team

Machine Learning Researcher, Multimodal LLMs Location: San Francisco, CA or Remote (US) 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

Role Description This is a remote position with occasional travel. You will own all machine learning and computer vision work on the team. Your primary focus will be applying CV and ML to sensor data from operational wildfire aircraft to turn raw imagery into accurate, actionable outputs for the agencies on the ground. The work will grow in scope as the program matures and the data starts flowing. What You'll Own - Design and train CV/ML models to extract meaningful fire intelligence from sensor data - Establish model validation workflows using ground-truth perimeter data from host agencies - Iterate on model accuracy by fire type, terrain, and atmospheric condition - Shape the roadmap for how ML can extend beyond detection into predictive and operational analytics - Work with internal Coulson IT on model deployment and inference infrastructure - Contribute to the strategy for how real-time video processing can extend the system’s operational value Qualifications - 3+ years applied ML or CV engineering in a production environment - Strong experience with image segmentation, object detection, or scene classification (PyTorch, TensorFlow, or equivalent) - Comfortable taking a model from training through to deployed inference - Experience working with noisy, real-world sensor data rather than clean benchmark datasets - Ability to define and track model performance metrics independently Nice to Have - Experience with thermal or infrared imagery — satellite, aerial, or ground-based - Background in remote sensing, geospatial ML, or environmental monitoring - Familiarity with real-time or near-real-time video inference pipelines - Understanding of fire behavior, wildland fire operations, or related domains - Experience with edge or embedded inference (onboard processing) - Familiarity with photogrammetry or structure-from-motion pipelines - Preference for west coast location. Extra points for PNW A Note on the Role This role is intentionally broad for a small team. You will be the only ML engineer for a significant period. The ideal candidate is someone who has built models for real-world problems and is energized by working close to the operational use case rather than at arm's length from it.

• The AI/ML Engineer plays a critical role in designing, developing, and deploying machine learning models and AI-driven solutions to support strategic business initiatives. • The role involves collaborating with cross-functional teams, including software engineering, data analytics, product development, and business stakeholders, to drive intelligent automation, data-driven decision-making, and advanced analytics capabilities. • Model Development and Optimization: Design, build, and deploy ML models for classification, regression, NLP, computer vision, or time-series forecasting. • Data Preparation and Feature Engineering: Clean, preprocess, and transform structured and unstructured datasets for training and inference. • Package and deploy models using tools like Docker, Flask/FastAPI, and Kubernetes. • Work with business stakeholders to translate real-world problems into AI/ML use cases. • Stay updated with the latest research, frameworks, and tools in machine learning and AI. • Collaborate with software developers, DevOps, data analysts, and domain experts for end-to-end solution delivery. • Maintain comprehensive documentation for models, experiments, and pipelines.