Staff AI Engineer

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Blip

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1999

📣 Marketing

☁️ SaaS

🔌 API

💰 $60M Series C - Blip on 2024-11

Marketing • SaaS • API

Blip is a SaaS platform that helps businesses create, customize, shorten, and track URLs and QR codes, and build mobile-friendly landing pages and link-in-bio pages. The platform provides branded links, dynamic QR codes, 2D barcodes, analytics and UTM tracking, integrations, and a developer API to manage and measure digital connections across marketing, packaging, and customer communications.

📋 Description

• Lead and execute the end-to-end lifecycle of language models and AI solutions (APIs, MCPs, Agents) at Blip. • Evaluate and orchestrate transitions between calls to commercial model APIs and internally distilled models deployed in a VPC, aiming for maximum quality, technological autonomy, and cost/latency efficiency. • Design and run rigorous experiments to test new architectures, quantization techniques, and modeling strategies under technical uncertainty. • Build and manage automated large-scale data cleaning and curation pipelines, and orchestrate and monitor inference workloads in cloud environments. • Work closely with product and business teams to ensure AI initiatives align with Blip's strategic objectives.

🎯 Requirements

• Academic background: Systems Engineering, Computer Science, Computer Engineering, Artificial Intelligence, or related fields. • Hands-on experience building Teacher–Student architectures, PEFT techniques (LoRA, QLoRA), and adapting/specializing open models for new capabilities. • Technical ability to compare and integrate proprietary model APIs as well as deploy and customize open models, knowing when to transition between them based on maturity and use-case requirements. • Experience in high-performance serving of language models and quantization techniques. • Ability to extract, process, and sanitize large volumes of data, generate high-fidelity synthetic data, and curate training/validation datasets. • Practical experience with cloud environments and Big Data tools for engineering, analysis, and curation of large-scale datasets, as well as orchestration of microservices and scalable inference engines in production. • Skill in building Golden Datasets, strict LLM-as-a-Judge frameworks, empirical evaluation and alignment/quality metrics in addition to traditional NLP evaluation metrics. • Proficiency in Python, PyTorch, efficient GPU utilization, and scalable API and microservice architectures. • Ability to operate in ambiguous scenarios, rapidly formulate and test hypotheses, discard infeasible approaches, and focus on efforts that deliver real business value. • Ability to connect R&D advances directly to product needs, turning papers and proofs of concept into productionized capabilities. • Ability to stay up to date with and maintain relevant knowledge amid the fast-changing LLM and generative AI market. • Ability to make data-driven decisions balancing Quality vs. Latency vs. Compute Cost vs. Privacy. • Ability to act as a technical reference within the AI Directorate, mentoring engineers and bridging research vision with business strategy. • Ability to translate complex Deep Learning concepts, research hypotheses, and infrastructure optimizations into clear ROI and strategic arguments for leadership.

🏖️ Benefits

• Health insurance • Retirement plans • Paid time off • Flexible work arrangements • Professional development