Strategic Project Lead- MultiModal
Turing
| Company | Turing |
| Category | Operations & Admin |
| Location | Colombia |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Lead |
| Salary | Not stated by the employer |
| Posted | 8 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
About Turing
Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. Turing accelerates frontier research with high-quality data, specialized talent, and training pipelines that advance thinking, reasoning, coding, multimodality, and STEM. For enterprises, Turing builds proprietary intelligence systems that integrate AI into mission-critical workflows, unlock transformative outcomes, and drive lasting competitive advantage.
Recognized by Forbes, The Information, and Fast Company among the world’s top innovators, Turing’s leadership team includes AI technologists from Meta, Google, Microsoft, Apple, Amazon, McKinsey, Bain, Stanford, Caltech, and MIT. Learn more at www.turing.com Strategic Project Lead
Multimodal AI
Turing | Remote – LATAM | US Time Zone | Full-Time (CLT)
About the Role
Turing is building the infrastructure layer for AI model development — delivering large-scale, production-grade data solutions across the most technically demanding AI programs in the world. As AI systems evolve beyond text into richer perceptual modalities — vision, image and video reasoning, generation, and audio — the need for specialists who can sit at the intersection of technical depth, delivery precision, and client trust has never been greater.
The Multimodal AI SPL is a senior individual contributor and delivery leader who owns end-to-end program outcomes for clients at the frontier of multimodal model development. You will operate across vision, video, image generation, and audio workflows — building scalable annotation and evaluation pipelines, translating complex model requirements into executable production systems, and serving as the primary strategic partner for key accounts in the US market.
This role is based in LATAM and aligned to US business hours.
What You'll Do
Client Partnership & Account Strategy
Serve as the primary point of contact and strategic advisor for US-based clients across multimodal AI programs — owning the client relationship from scoping through delivery and expansion
Translate complex, evolving model requirements across vision, video, image, and audio modalities into structured program plans with clear milestones, quality benchmarks, and risk posture
Lead strategic conversations with client research and engineering teams, providing informed perspective on data quality trade-offs, modality-specific constraints, and delivery architecture choices
Identify and develop account growth opportunities by understanding client roadmap priorities and proactively surfacing adjacent capability fits
Program Architecture & Cross-Modal Delivery
Design and own end-to-end delivery architecture for multimodal annotation, evaluation, and RLHF programs — spanning visual understanding, image/video generation quality, spatial reasoning, and audio-visual alignment
Build scalable execution systems that account for the distinct complexity of each modality — frame-level video annotation, multi-step image generation evaluation, audio-visual consistency, and intermodal reasoning tasks
Drive program governance including milestone tracking, escalation frameworks, and quality gates that span multiple concurrent workstreams
Anticipate and resolve cross-functional dependencies across annotation, QC, tooling, and research teams before they become blockers
Quality Systems & Model Alignment
Own the quality architecture for multimodal programs — defining rubrics, calibration cycles, inter-annotator agreement protocols, and escalation logic tailored to modality-specific failure modes
Partner with client ML and research teams to understand model evaluation criteria and ensure ground truth data aligns with downstream training and fine-tuning objectives
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