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AI/ML Lead

TechTriad

πŸ“ San Francisco, California, US0πŸ’Ό Full-timeπŸ• 5/23/2026
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Role overview

TechTriad is hiring for the AI/ML Lead role in San Francisco, California, US. It is full-time, Lead level, in the Tech sector. It was posted 5/23/2026.

On TalentyGo you can review this job and apply more effectively: Charlie prepares an ATS-optimized resume and a cover letter tailored to "AI/ML Lead" at TechTriad in about a minute. Before you apply, you can also check how well your profile fits, with a match score based on skills, experience, location and seniority.

Role
AI/ML Lead
Company
TechTriad
Location
San Francisco, California, US
Work mode
On-site
Employment
Full-time
Seniority
Lead
Sector
Tech
Posted
5/23/2026

Description

πŸš€ Hiring: ML Engineering Lead πŸ“ San Francisco, CA (Hybrid) πŸ•’ Full-Time We’re looking for a hands-on ML Engineering Lead to join our core engineering team and help shape the future of AI-powered products. This is an opportunity to lead cutting-edge ML/AI initiatives in an early-stage, high-impact environment where you’ll own technical direction from strategy to production. πŸ”Ή What You’ll Do: β€’ Lead the ML/AI function end-to-end across the organization β€’ Architect and scale multi-modal, agentic AI systems β€’ Drive development of LLM-powered workflows and intelligent agents β€’ Define evaluation frameworks, datasets, and performance metrics β€’ Ensure production reliability, scalability, and compliance of ML systems β€’ Mentor and grow a high-performing ML engineering team β€’ Collaborate closely with product and engineering leadership πŸ”Ή What We’re Looking For: βœ” 8+ years of ML/AI engineering experience βœ” Strong experience with LLMs, Generative AI, and agentic systems βœ” Expertise in deep learning, transformers, model evaluation, and ML fundamentals βœ” Experience deploying and scaling ML systems in production environments βœ” Strong leadership and architecture decision-making skills βœ” Ability to thrive in fast-paced, ambiguous startup environments ⭐ Bonus Skills: β€’ RAG systems, orchestration frameworks, long-context reasoning β€’ Fine-tuning open-source & closed LLMs β€’ Full-stack ML exposure (data β†’ infra β†’ APIs) β€’ Strong product mindset with focus on real-world impact

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