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Member of Technical Staff - Forward Deployed Engineer

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Liquid AI

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Location:
United States , San Francisco, Boston

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Contract Type:
Not provided

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Salary:

Not provided

Job Description:

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.

Job Responsibility:

  • 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

Requirements:

  • 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)
  • 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

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 we offer:
  • Competitive base salary with equity in a unicorn-stage company
  • We pay 100% of medical, dental, and vision premiums for employees and dependents
  • 401(k) matching up to 4% of base pay
  • Unlimited PTO plus company-wide Refill Days throughout the year

Additional Information:

Job Posted:
February 21, 2026

Employment Type:
Fulltime
Work Type:
Hybrid work
Job Link Share:

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