Liquid AI

Software Engineer

• Act as the technical owner for enterprise customer VLM post-training engagements.
• Translate customer requirements into concrete multimodal post-training specifications and workflows.
• Design and execute visual data generation, filtering, and quality assessment processes, including image-text pair curation, annotation pipelines, and synthetic data generation for visual tasks.
• Run supervised fine-tuning, preference alignment, and reinforcement learning workflows for vision-language models.
• Design task-specific evaluations for visual understanding, grounding, OCR, document parsing, and other multimodal capabilities. Interpret results and feed learnings back into core post-training pipelines.

Member of Technical Staff – Post Training, Applied Vision

• Act as the technical owner for enterprise customer engagements involving recommendation and ranking workloads
• Translate customer requirements into concrete specifications for recommendation models
• Design and execute data pipelines for user interaction data, feature engineering, and training data curation at scale
• Fine-tune and adapt large-scale sequential recommendation models (e.g., HSTU-style architectures) for customer-specific use cases
• Design task-specific evaluations for recommendation model performance (ranking quality, latency, throughput) and interpret results
• Build reusable applied tooling and workflows that accelerate future customer engagements

Member of Technical Staff – Applied ML, RecSys

• Act as the technical owner for enterprise customer post-training engagements involving audio and speech workloads
• Translate customer requirements into concrete post-training specifications for ASR, TTS, and speech-to-speech tasks
• Design and execute data generation, preprocessing, augmentation, and quality filtering processes for audio corpora
• Fine-tune and adapt audio/speech models for customer-specific use cases, owning delivery from requirements through deployment
• Design task-specific evaluations for audio model performance (noise robustness, speaker variation, latency) and interpret results
• Build reusable applied tooling and workflows that accelerate future customer engagements

Member of Technical Staff – Post Training, Applied, Audio

• The VLM team builds vision-language models that run on-device, under tight latency and memory constraints, without sacrificing quality.
• Join a focused, hands-on group that works directly on models and collaborates closely with our pretraining, post-training, and infrastructure teams.
• Lead a new model capability end-to-end from task spec through data curation, training recipe, ablations, evaluation, and into the final shipped model.
• Improve visual reasoning through reinforcement learning and preference optimization methods.
• Push the quality-efficiency frontier on token efficiency via encoder/connector design.

Member of Technical Staff – Multi-Modal, Vision

• Implement and optimize inference kernels for CPU, NPU, and GPU architectures across diverse edge hardware
• Develop quantization strategies (INT4, INT8, FP8) that maximize compression while preserving model quality under strict memory budgets
• Contribute to llama.cpp and other open-source inference frameworks, including new model architectures (audio, vision)
• Profile and optimize end-to-end inference pipelines to achieve sub-100ms time-to-first-token on target devices
• Collaborate with ML researchers to understand model architectures and identify optimization opportunities specific to Liquid Foundation Models

Technical Staff Member – Edge Inference Engineer

• Profile, optimize, and scale RL training runs to reduce iteration time
• Integrate new optimization techniques as they emerge from the research community
• Design and implement tools and environments that test the boundaries of model capabilities
• Turn proof-of-concept ideas into robust training pipelines and best-in-class models

Member of Technical Staff – Reinforcement Learning

Account Executive

• Own the full sales cycle from prospecting and outbound through negotiation, close, and handoff
• Build and manage a qualified pipeline across target verticals, using modern outbound tools and your own network to increase deal velocity
• Lead discovery calls, product demos, and technical deep-dives with our engineering team, including proof-of-concept evaluations and benchmarking cycles
• Navigate complex enterprise procurement across legal, security, and technical evaluation stakeholders
• Partner with founders on pricing, packaging, and deal structures for enterprise engagements
• Feed frontline market intelligence back to product and leadership: objections, competitor signals, pricing feedback, and use-case trends
• Build our sales playbook: document what works, create repeatable processes, and lay the foundation for scaling the GTM team

Founding Account Executive

• Act as the technical owner for enterprise customer post-training engagements
• Translate customer requirements into concrete post-training specifications and workflows
• Design and execute data generation, filtering, and quality assessment processes
• Run supervised fine-tuning, preference alignment, and reinforcement learning workflows
• Design task-specific evaluations, interpret results, and feed learnings back into core post-training pipelines

Member of Technical Staff – Post Training, Applied

Product Manager

• Own one or more GTM-ready solutions end-to-end, from definition through scalable customer deployment
• Extract learnings from customer engagements and identify patterns that can be productized
• Work with ML and inference teams to build tooling that collapses implementation overhead
• Define ICPs, pricing, and packaging for repeatable solutions
• Partner with GTM teams to scale outbound sales motions around productized offerings