Member of Technical Staff - Forward Deployed Engineer at Liquid Ai

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 You will work directly on customer engagements that generate revenue. This is hands-on technical work: fine-tuning Liquid Foundation Models (LFMs) for enterprise deployments across text, vision, and audio modalities. You will own technical delivery end-to-end, working with customers to understand their data and constraints, then hitting quality and latency targets on real hardware. This is not API wrapper work. You will fine-tune models, generate and curate training data, debug failure modes, and deploy to devices with real latency and memory constraints. WHAT WE'RE LOOKING FOR We need someone who: - Fine-tunes models: You have hands-on experience with techniques like LoRA, PEFT, DPO, instruction tuning, or RLHF. You've written training loops, not just API calls. - Works with modern architectures: Your experience includes models released in the last 12-18 months (Llama 3.x, Mistral, Gemma, Qwen, etc.), not just BERT or classical ML. - Generates and curates data: You've created synthetic training data to address specific model failure modes. You understand how data quality drives model performance. - Debugs methodically: When a model underperforms, you diagnose whether it's a data problem, architecture problem, or training problem, and you fix it. - Ships to customers: You can translate ambiguous customer requirements into concrete technical specs and deliver against quality metrics. - Contributes to open source: You have a Hugging Face profile, PyPI packages, or OSS contributions that demonstrate depth, not just off-the-shelf usage. THE WORK - Fine-tune LFMs on customer data to hit quality and latency targets for on-device and edge deployments - Generate and curate training data to address specific model failure modes - Run experiments, track metrics, and iterate until customer success criteria are met - Translate ambiguous customer requirements into concrete technical specifications - Provide analytics to commercial teams for contract structuring and pricing - Work across text, vision, and audio modalities as customer needs require DESIRED EXPERIENCE Must-have: - Hands-on fine-tuning experience with modern LLMs (last 12-18 months): LoRA, PEFT, DPO, instruction tuning, or similar - Strong ML fundamentals: you understand how models learn, fail, and improve - Experience generating or curating training data to address model gaps - Autonomous coding and debugging skills in Python and PyTorch - Proficiency with open-source ML ecosystem (Hugging Face transformers, datasets, accelerate) Nice-to-have: - Experience delivering ML work to external customers with measurable outcomes - Experience with inference optimization (vLLM, SGLang, TensorRT, llama.cpp) - Post-training experience: RLHF, DPO, reward modeling WHAT SUCCESS LOOKS LIKE (YEAR ONE) - You have owned technical delivery for at least one engagement that closed a contract - Your work directly contributed to measurable B2B revenue - Commercial teams have the metrics they need to price deals accurately because you provided them - You've ramped on at least one new modality beyond your primary expertise WHAT WE OFFER - Real ML work: You will fine-tune models, generate data, and ship solutions, not just configure API calls - 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