Strategic Project Lead- MultiModal

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
• Establish feedback loops between human evaluation signals and model performance metrics, enabling data-driven iteration on annotation schema and quality thresholds
• Maintain accountability for data quality outcomes at the program level, not just task level

Talent & Team Capability

• Build and develop delivery teams with the right modality-specific expertise — sourcing, assessing, and onboarding annotators, QC reviewers, and leads who can operate across vision, video, and audio tasks
• Create structured onboarding and calibration programs that ramp team proficiency quickly on technically complex modality combinations
• Drive a performance culture oriented around quality ownership, cross-modal adaptability, and continuous improvement

Operational Intelligence & Scalable Execution

• Develop and maintain program-level delivery intelligence — throughput metrics, quality trends, cost efficiency, and capacity utilization — that enables proactive decision-making and client reporting
• Build repeatable execution playbooks for multimodal program types that can be scaled across new accounts and modality configurations
• Continuously evolve operational frameworks in response to new model architectures, annotation paradigms, and client program structures

What We're Looking For

• 6–10 years of experience in program or project management within AI/ML, data operations, or a closely adjacent technical domain
• Demonstrated exposure to two or more of the following in a production context: computer vision, image generation/evaluation, video annotation or QA, audio processing, or multimodal reasoning tasks
• Proven ability to manage external client relationships at a technical level — comfortable owning a client conversation independently without requiring escalation support
• Experience building and leading cross-functional delivery teams in fast-paced, ambiguous environments
• Strong analytical instincts — able to diagnose delivery and quality issues from first principles, not just apply known solutions
• Fluency in English at a professional level suitable for US client-facing engagement; availability to work US business hours from LATAM

What Makes You Stand Out

• Experience at an AI lab, foundation model company, or RLHF-focused data services provider
• Hands-on familiarity with multimodal annotation tooling, evaluation frameworks (e.g., CLIP-based scoring, preference data collection, VQA benchmarks), or model card evaluation pipelines
• Prior work on image or video generation quality programs — understanding of prompt fidelity, artifact detection, temporal consistency, or aesthetic evaluation
• Background in managing global teams distributed across time zones, with a track record of building delivery culture remotely
• Comfort engaging technical counterparts (ML engineers, research scientists) as a peer, not just a project coordinator

Why Turing

Turing operates at the earliest and most technically demanding phase of AI model development. The programs you'll lead here directly shape how the next generation of multimodal models perceive, reason, and generate across modalities — before those capabilities are widely understood. You'll work with clients who are defining the frontier, on programs that don't have established playbooks, with a team that moves fast and takes ownership seriously.

If you're a builder who wants to work on the hardest problems in AI data at scale — and you want to do it from LATAM while building deep relationships with the US teams driving the industry forward — this is the role.