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Selected role

AI Machine Learning Engineer [207605]

Aquent California, United States

Senior Full-time

Join a globally recognized leader in entertainment and technology, partnering with Aquent, that is at the forefront of innovation, constantly pushing the boundaries of creativity and technological advancement. This company is dedicated to delivering unparalleled experiences and content that captivate audiences worldwide.

We are seeking a visionary and highly skilled Generative AI & Machine Learning Engineer to join our dynamic team. In this pivotal role, you will be instrumental in shaping the future of interactive content, personalized experiences, and cutting-edge media creation. Your expertise will directly impact how our audiences engage with our products, bringing imaginative concepts to life through advanced AI and transforming imaginative concepts into tangible, interactive realities.

This role is 100% Remote but candidates must be located near Orlando, FL, Los Angeles, CA, or Seattle, WA.

You will play a critical role in developing and deploying next-generation generative AI systems, from concept to production, ensuring brand safety, ethical considerations, and robust performance. This is an opportunity to innovate at scale, influencing how millions experience content and interact with digital environments.

What You’ll Do:**

As a Generative AI & Machine Learning Engineer, you will be at the heart of our innovation, driving the creation of groundbreaking AI solutions. Your contributions will directly enhance user experiences, streamline content creation, and ensure responsible AI deployment.

Pioneer Generative Systems: Build sophisticated text-to-image and text-to-video generation systems, alongside advanced speech synthesis and voice cloning models with integrated safety guardrails for authentic character voices.
Enhance Content Understanding: Develop robust image-to-text and video-to-text systems to power insightful content analysis and improve accessibility.
Innovate Cross-Modal Experiences: Implement cutting-edge cross-modal generation capabilities, such as transforming text and images into video, or audio and text into rich multimedia content.
Drive Real-Time Interactions: Create real-time generative systems that enable dynamic and interactive experiences.
Ensure Model Quality & Safety: Design and implement custom evaluation models for content assessment, including brand safety, content ratings, and character consistency.
Automate Performance Benchmarking: Build automated benchmarking systems to rigorously evaluate generative model performance across diverse cloud environments.
Develop Ethical AI Pipelines: Create specialized ML pipelines for hallucination detection, bias measurement, and factual accuracy assessment, ensuring responsible AI.
Craft Domain-Specific Evaluation: Develop tailored evaluation frameworks for specific use cases, focusing on content appropriateness, brand alignment, and safety compliance.
Integrate Human-in-the-Loop: Implement human-in-the-loop evaluation systems, collaborating with domain experts to refine and validate AI outputs.
Advance AI Techniques: Implement cutting-edge generative AI techniques, including diffusion models, transformer variants, and mixture of experts architectures.
Champion AI Safety: Develop constitutional AI and AI safety techniques to ensure responsible content generation.
Strengthen Model Robustness: Build adversarial training systems to significantly improve model resilience and performance.
Optimize Prompt Engineering: Research and implement advanced prompt engineering and in-context learning optimization strategies.
Design Novel Architectures: Create innovative architectures tailored for specific generative tasks, pushing the boundaries of what’s possible.
Optimize for Production: Design A/B testing frameworks for continuous generative model comparison and optimization.
Achieve Real-Time Inference: Build real-time inference optimization solutions for low-latency content generation.
Scale Model Serving: Implement robust model serving infrastructure with auto-scaling and load balancing capabilities.
Ensure Continuous Performance: Create comprehensive model monitoring, drift detection, and automatic retraining systems.
Enhance AI Performance: Develop caching and retrieval systems to significantly improve generative AI performance and efficiency.
Must-Have Qualifications:**
Generative AI & Deep Learning:
5+ years of hands-on machine learning engineering experience, with at least 2 years specifically focused on generative AI.
Strong experience with transformer architectures, diffusion models, and large language models.
Proven track record with model fine-tuning, Reinforcement Learning from Human Feedback (RLHF), and parameter-efficient training techniques.
Experience with multi-modal AI systems (text+vision, text+audio, cross-modal generation).
Deep understanding of generative AI training dynamics, loss functions, and optimization techniques.
Technical Expertise:
Expert-level Python programming proficiency, utilizing leading deep learning frameworks such as TensorFlow and PyTorch, and distributed training frameworks like DeepSpeed, Accelerate, and Ray.
Experience with leading cloud ML platforms and model serving solutions like TensorRT, ONNX, and TorchServe.
Strong background in computer vision, Natural Language Processing (NLP), and audio processing for generative applications.
Knowledge of MLOps principles, model versioning, and production deployment strategies, including orchestration tools like Kubernetes, Docker, APIGEE, and Terraform.
Experience with vector databases such as Pinecone and Weaviate, embeddings, and Retrieval-Augmented Generation (RAG).
Familiarity with specialized AI frameworks like Autogen, LangChain, and Model Context Protocol (MCP), and monitoring platforms such as MLflow and Weights & Biases.
AI Safety & Evaluation:
Experience building robust evaluation frameworks for generative AI systems.
Knowledge of AI safety techniques, including bias detection, content filtering, and adversarial robustness.
Understanding of responsible AI frameworks and red teaming methodologies.
Familiarity with AI governance, model interpretability, and compliance requirements.
Education: Bachelor’s Degree in Machine Learning, Computer Science, or a related technical field.
Nice-to-Have Qualifications:**
Advanced degree (Master’s or Ph.D.) in Machine Learning, Computer Science, or a related field.
Experience with industry applications such as content creation, media analysis, or interactive systems.
Knowledge of edge AI optimization and real-time inference systems.
Background in reinforcement learning and human preference modeling.
Experience with large-scale distributed training (multi-GPU, multi-node environments).
Contributions to open-source AI projects or published research in generative AI.
About Aquent Talent:** Aquent Talent connects the best talent in marketing, creative, and design with the world’s biggest brands. Our eligible talent get access to amazing benefits like subsidized health, vision, and dental plans, paid sick leave, and retirement plans with a match. More information on our awesome benefits! Aquent is an equal-opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other legally protected characteristics. We’re about creating an inclusive environment—one where different backgrounds, experiences, and perspectives are valued, and everyone can contribute, grow their careers, and thrive.

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