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Open nowPosted 321 days ago

Founding AI/Machine Learning Engineer

Trifetch4 open roles

Where
San Francisco, CA
Work mode
On site
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Your applicationOpen nowFounding AI/Machine Learning EngineerTrifetch · San Francisco, CA

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This job: posted 321 days ago

The posting

What You Will Bring

  • Deep Post-Training Expertise: Hands-on experience with post-training models for specific applications (SFT, RLHF, RLAIF, Reward Modeling, Knowledge Distillation, etc)
  • Strong Architectural Foundations: Deep understanding of Transformer architectures (attention mechanisms, positional encodings) and ML systems. And knowing when to use which model (sometimes it's simpler models that win!)
  • Large-Scale Training: Experience with distributed training frameworks and optimizing training jobs on GPU clusters.
  • High Velocity & Ownership: You thrive in ambiguous environments, learn quickly, and have a bias toward action. You are comfortable shipping in rapid cycles typical of early-stage startups.
  • Technical Stack: Proficiency in Python, PyTorch. Familiarity with the modern open-source LLM stacks (HuggingFace, Vertex, Vercel, etc.).

(Healthcare experience is preferred but not strictly required if you have exceptional ML fundamentals.)

What You’ll Work On

Architect the Post-Training Stack: Lead the design and execution of alignment pipelines (SFT, RLHF, RLAIF) that bridge the gap between "exam-passing" models and "clinically useful" systems.

  • Leverage Proprietary Data: Utilize our proprietary and open source medical datasets to fine-tune models on edge cases that generic models miss.
  • Novel Technique Experimentation: Research and implement cutting-edge post-training methods to optimize model performance, aiming for improvements in calibration and reliability critical for healthcare.
  • Safety & Evaluation: Build rigorous evaluation frameworks (LLM-as-a-judge, benchmarks) to detect hallucinations, ensure clinical correctness, and guarantee safety before deployment.
  • Strategic Collaboration: Work directly with the co-founders to define the research roadmap and platform strategy.

Bonus Points

  • Research Track Record: Published research in high-impact journals or top-tier ML/AI conferences (NeurIPS, ICML, ICLR, CVPR, ACL).
  • Top Lab Experience: Background working or interning at top research labs (e.g., FAIR, DeepMind, OpenAI, Google DM, MSR, Stanford/CMU/MIT labs).
  • Domain Expertise: Experience dealing with multimodal health data, clinical reasoning, or safety-critical ML systems.
  • Entrepreneurial Spirit: You have founded a company, built early-stage products, or enjoy the "zero-to-one" phase of building.
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