Our mission is to build state-of-the-art, general-purpose AI systems that are capable, efficient, highly aligned, and trustworthy.

Industry: Information Services
Company Size: 11-50 employees

Liquid AI

Savvy Admin PROD

**About Liquid Labs**

Research has been core to Liquid AI from the beginning.

Liquid Labs gives that work a formal home; an internal research accelerator driving fundamental breakthroughs in the science of building intelligent, personalized, and adaptive machines.

Our origins trace back to MIT CSAIL, where the foundational work on Liquid Neural Networks defined a new class of dynamical, efficient sequence-processing architectures. That research became the basis for Liquid Foundation Models (LFMs). Scalable, multimodal models built for real-world deployment in resource-constrained environments.

At Liquid Labs, we extend that lineage - pushing forward the frontier of efficient, adaptive intelligence through both fundamental research and practical engineering.

We work hand-in-hand with Liquid’s core foundation model and systems teams to translate theory into deployed capability — defining a new generation of intelligent systems that are both powerful and efficient.

**About The Role**

As a Research Engineer, you’ll join a small, high-context team exploring the limits of adaptive intelligence. You’ll design and implement novel architectures, training methods, and inference strategies to redefine what efficient AI can do.

You’ll operate at the intersection of research and engineering — translating scientific ideas into working systems, publishing where it drives the field forward, and deploying where it changes what’s possible.

~~**While San Francisco and Boston are preferred, we are open to other locations in the United States.** ~~**This Role Is For You If**

- Work fluently in Python and frameworks such as PyTorch, JAX, or TensorFlow

- Have experience in machine learning research or production-grade ML systems

- Move fast from paper to prototype — curiosity backed by precision

- Care about efficiency, scalability, and elegant system design as scientific principles

- Value small, deep-technical teams where impact is immediate and measurable

- Have a track record of publication in tier-1 venues (NeurIPS, ICML, ICLR, CVPR, ACL, or equivalent), demonstrating original contribution and research rigor

**Open Science and Impact**

Liquid Labs reinforces our commitment to transparent, reproducible, open research.

We publish through technical reports, architectural deep dives, ablations, and model releases, advancing the broader science of efficient AI while translating breakthroughs into production-ready systems.

**Why Liquid Labs**

Liquid Labs is for researchers who build.

Those who care about lasting impact more than publication count, but who hold themselves to the same scientific standard.

We don’t chase benchmarks; we redefine them.

We move fast, think deeply, and measure success by the systems that endure.

~~**There is no application deadline. We review candidates on a rolling basis. ** ~~

Liquid Labs - Research Engineer

**About Liquid AI**

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.

The Opportunity

Liquid AI is building a solutions architecture function from scratch. You will be one of the first SAs, working directly with the Head of Solutions Architecture and across the go-to-market org to own customer engagements end-to-end.

Our models are purpose-built for environments where memory, latency, and power are binding constraints - edge devices, mobile, embedded systems, and on-prem infrastructure where frontier models simply cannot run. You will work at this boundary every day.

Customers range from AI-native companies to enterprise organizations exploring AI for the first time. Your job is to bridge the gap between what our models can do and what customers believe is possible, then deliver on that promise from technical validation through go-live.

What We're Looking For

**We Need Someone Who**

- Technical builder: You can download a model, build a demo, and present it to a customer. You are as comfortable in a Jupyter notebook as you are in a boardroom.

- Creative problem solver: You see opportunities where customers see limitations. You can take a small, efficient model and show an enterprise why it changes their cost structure or enables something they did not think was possible.

- End-to-end owner: You do not draw a line between 'pre-sales' and 'post-sales.' You own the outcome from first call to go-live and beyond.

- Org builder: You want to build a function, not inherit one. You will create playbooks, demo libraries, and engagement processes that scale as the team grows.

- Imagination-gap closer: Enterprise buyers often cannot envision what a fine-tuned small model can do at middleware speeds. You don't just demo—you reframe what's possible on hardware they already own.

The Work

- Own customer engagements end-to-end: from qualified opportunity through technical validation, go-live, and ongoing delivery across all customer segments

- Build customer-specific demos and proofs-of-concept using Liquid models (including LEAP for fine-tuning, domain adaptation, and evaluation) to drive technical wins

- Lead technical discovery: map current-state customer architectures to Liquid solutions, drive competitive positioning against open-source and incumbent models, and quantify ROI for both cost-optimization and new-experience use cases

- Co-own the product-field feedback loop: document friction patterns, eval failures, and capability gaps from engagements and partner with product and research to influence roadmap

- Turn engagement learnings into reusable assets: reference architectures, solution primitives, demo building blocks, engagement playbooks, and vertical-specific solution patterns across Liquid's priority industries

Desired Experience

**Must-have**

- Applied ML skills: hands-on experience working with ML models in customer-facing contexts (building demos, prototypes, or production integrations)

- Pre-sales and post-sales experience: you have owned technical customer engagements end-to-end, not just the pitch

- Strong customer-facing communication: you can run discovery, build relationships with technical and business buyers, and present to executives

- Understanding of AI architectures and deployment tradeoffs: token efficiency, on-device vs. cloud, model size vs. latency, open-weight vs. proprietary

**Nice-to-have**

- Familiarity with small or efficient model deployment (edge, on-device, latency-constrained environments)

- Track record of creating thought leadership content, technical blogs, or presenting at industry events

- Familiarity with efficient model deployment: quantization (INT4/INT8, GGUF, AWQ), model serving frameworks (vLLM, TensorRT-LLM, llama.cpp), and hardware-aware optimization for edge or latency-constrained environments

- Experience designing and debugging model evaluations—you understand why benchmark results can diverge from production performance and know how to diagnose the root cause

What Success Looks Like (Year One)

- Qualified opportunities convert to technical wins faster, with a measurable improvement in the qualified-to-win rate

- A library of scalable demos, engagement playbooks, and customer-facing collateral exists and is actively used

- A structured feedback loop from customer conversations to the product and model teams is established and influencing roadmap decisions

What We Offer

- Build the function: You are defining how Liquid goes to market technically, with direct influence on product direction and access to the founding team.

- Compensation: Competitive base salary with equity in a unicorn-stage company

- Health: We pay 100% of medical, dental, and vision premiums for employees and dependents

- Financial: 401(k) matching up to 4% of base pay

- Time Off: Unlimited PTO plus company-wide Refill Days throughout the year

Solutions Architect

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