Member of Technical Staff
Liquid AI
Member of Technical Staff
Liquid AI
Provides generative, edge-capable foundation models
Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.
On Liquid’s Applied ML team, you’ll adapt our audio models for enterprise products where a spoken request needs to trigger the right action. When someone asks a voice assistant to “turn down the passenger-side temperature,” the model needs to select the right tool, pass the right arguments, and confirm what changed, even in a noisy cabin.
You’ll work directly with customers to define what reliable behavior looks like, then own the data, post-training, evaluation, and delivery needed to achieve it. What you learn from those deployments will help improve Liquid’s audio models and post-training pipelines.
We need someone who:
Defines success before training. You ask customers for examples of what should happen, what can go wrong, and which mistakes matter most. You turn those answers into tests both teams can use to make a release decision.
Investigates failures hands-on. When a model takes the wrong action, you listen to the audio, inspect the predicted call and training examples, and design an experiment that can distinguish between likely causes.
Makes tradeoffs clear. You explain what the evidence supports, where uncertainty remains, and what you recommend next. If a quality target conflicts with a device’s latency or memory budget, you help the customer choose a workable path.
Lead the technical work on enterprise audio engagements, from scoping the customer’s use case and target hardware through model delivery.
Build evaluations that combine automated checks with human listening. Measure tool selection, argument accuracy, unnecessary calls, task completion, and response latency across speakers and noise conditions.
Build and curate speech and text datasets from real and synthetic dialogue. Cover requests the model should act on, ambiguous requests that need clarification, and cases where it should take no action.
Fine-tune audio models to translate speech into structured tool calls and natural spoken responses. Choose supervised fine-tuning, preference optimization, or reinforcement learning based on the behavior you need to improve.
Test on the target device, review failures with the customer, and bring reusable datasets, evaluations, and training improvements back into Liquid’s core pipelines.
What you’ll need
Hands-on experience post-training a language or multimodal model. You can explain the training objective you chose, how the data influenced the result, and how you investigated a regression.
Experience designing task-specific evaluations, breaking results down by failure mode, and using the findings to decide what to change.
Experience building and validating training datasets, including assessing coverage, label quality, and gaps between training examples and real usage.
Familiarity with function calling, tool use, or structured model outputs, including how to check whether a model selected the intended action and supplied valid arguments.
Helpful, not required
Experience fine-tuning or evaluating speech-to-speech, speech recognition, text-to-speech, or multimodal audio-text systems.
Experience delivering an applied ML project directly with a customer.
Hands-on work with preference optimization or reinforcement learning beyond supervised fine-tuning.
Experience evaluating models on devices with tight latency or memory budgets.
You’ve independently delivered an enterprise audio model that meets the customer’s agreed quality and latency criteria on the target hardware.
You’ve shipped a voice-driven workflow that selects the correct tool and arguments, asks for clarification when needed, and avoids unintended actions under realistic audio conditions.
Liquid’s audio or post-training team has adopted a dataset, evaluation suite, or training improvement from your work and used it beyond the original engagement.
A direct hand in model development. You’ll see where a voice model fails in a customer’s product and have the scope to improve the data, training, and evaluations behind it.
Compensation & Equity. Competitive base salary plus equity, so you share in what we build. Final offers reflect skills, experience, and location.
Time Off. Unlimited PTO, plus company-wide Refill Days, long weekends when the whole company rests together.
Health & Wellbeing. Comprehensive health coverage, with details varying by location.
Paid Leave. Paid leave for pregnancy, parenting, medical needs, and caring for family.
Liquid AI builds and deploys foundation models based on liquid neural networks, focusing on efficient, on-device AI. It develops Liquid Foundation Models (LFMs), a family of generative AI models designed to be smaller and more computation-efficient than typical large language models, enabling deployment on edge devices with lower latency, better privacy, and reduced infrastructure costs. The approach includes end-to-end AI expertise and customizable architectures for enterprises that require real-time performance and private processing. Compared to traditional AI providers, Liquid AI emphasizes edge-ready, adaptable models that run efficiently on constrained hardware, and it targets enterprise-grade, private, reliable AI solutions. The company’s goal is to enable real-time, on-device AI at scale for businesses by offering compact, capable foundation models and the tools to customize them for specific applications.
Explore more opportunities at Liquid AI