Career at Trifetch
Open Positions
Founding AI/Machine Learning Engineer
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.)
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.
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.
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.
