Member of Technical Staff - Post Training, Applied (Audio)
Owns enterprise audio post-training engagements, from requirements through delivery and evaluation.
On-site • San Francisco, CA, USA
- Full Time
Browse the full live job inventory for Liquid AI. This page is focused on current openings, while the overview tab highlights role groups and hiring patterns.
Owns enterprise audio post-training engagements, from requirements through delivery and evaluation.
Owns post-training engagements for text workloads, translates requirements into workflows, and builds reusable tooling for enterprise customers.
Owns VLM post-training engagements end-to-end, translating customer requirements into concrete multimodal post-training specifications and workflows.
Design and build core systems that make large training runs fast and reliable.
Lead competitive analysis and market research to inform positioning and surface growth opportunities.
Develop and fine-tune Japanese-language models, curate data, and evaluate LFMs for enterprise use cases.
Own one or more GTM-ready solutions end-to-end, from definition through scalable customer deployment.
Develop and optimize inference kernels for edge devices, targeting sub-100ms latency and efficient memory usage.
Provide high-level executive support, coordinating calendars, travel, events, and cross-functional follow-ups.
Bridge customer needs and Liquid capabilities through end-to-end technical engagement, demos, and ROI-focused validation.
Builds end-to-end data architecture and agent layer to unify company data graph and surface leadership-facing insights.
Profile, optimize, and scale RL training runs to reduce iteration time.
Design and ship custom CUDA kernels, profile at hardware level, and deliver speedups in production pipelines.
Owns open-source partnerships, develops tutorials and documentation to drive LFM adoption.
Lead a new model capability end-to-end from task spec through data curation, training recipe, ablations, evaluation, and into the final shipped model.
ML-minded engineers who collect, filter, and synthesize high-quality data at scale to impact model performance.
Strategic finance leader building GTM finance engine and revenue systems to enable scalable deal execution.
Design and implement novel architectures, training methods, and inference strategies to redefine what efficient AI can do.
Owns end-to-end data pipelines, evaluation systems, and customer deployments for on-device, low-latency audio models.
Owns applied ML work end-to-end for recommendation systems in enterprise production environments.