Machine Learning Engineer

Client

Location:

Remote - Candidates must be located near Orlando, FL;
Glendale, CA;
Seattle, WA; or Anaheim, CA

Starting: 02/19/2026

Pay Comments:

Minimum Pay (per hour): 81.04

Maximum Pay (per hour): 90.04

Hours: Full-time

Duration: 22 months

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.

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.

• 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.
• Develop robust image-to-text and video-to-text systems to power insightful content analysis and improve accessibility.
• Implement cutting‑edge cross-modal generation capabilities, such as transforming text and images into video, or audio and text into rich multimedia content.
• Create real-time generative systems that enable dynamic and interactive experiences.
• Design and implement custom evaluation models for content assessment, including brand safety, content ratings, and character consistency.
• Build automated benchmarking systems to rigorously evaluate generative model performance across diverse cloud environments.
• Create specialized ML pipelines for hallucination detection, bias measurement, and factual accuracy assessment, ensuring responsible AI.
• Develop tailored evaluation frameworks for specific use cases, focusing on content appropriateness, brand alignment, and safety compliance.
• Implement human-in-the-loop evaluation systems, collaborating with domain experts to refine and validate AI outputs.
• Implement cutting‑edge generative AI techniques, including diffusion models, transformer variants, and mixture of experts architectures.
• Develop constitutional AI and AI safety techniques to ensure responsible content generation.
• Build adversarial training systems to significantly improve model resilience and performance.
• Research and implement advanced prompt engineering and in-context learning optimization strategies.
• Create innovative architectures tailored for specific generative tasks, pushing the boundaries of what’s possible.
• Design A/B testing frameworks for continuous generative model comparison and optimization.
• Build real-time inference optimization solutions for low-latency content generation.
• Implement robust model serving infrastructure with auto-scaling and load-balancing capabilities.
• Create comprehensive model monitoring, drift detection, and automatic retraining systems.
• Develop caching and retrieval systems to significantly improve generative AI performance and efficiency.

• 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.