Mirage
Member of Technical Staff, ML Engineer
Join to apply for the Member of Technical Staff, ML Engineer role at Mirage.
This range is provided by Mirage. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $200,000.00/yr - $300,000.00/yr
About Mirage Mirage is the leading AI short‑form video company. We’re building full‑stack foundation models and products that redefine video creation, production and editing. Over 20 million creators and businesses use Mirage’s products to reach their full creative and commercial potential. We are a rapidly growing team of ambitious, experienced, and devoted engineers, researchers, designers, marketers, and operators based in NYC. As an early member of our team, you’ll have an opportunity to have an outsized impact on our products and our company’s culture.
About the role Captions is seeking a Machine Learning Engineer to partner closely with our Researchers and bring large‑scale multimodal video diffusion models into production. You’ll be responsible for optimizing and deploying state‑of‑the‑art generative models to deliver low‑latency, high‑throughput inference at scale, ensuring these innovations reach millions of creators worldwide.
Responsibilities
Inference & Deployment: Develop high‑performance GPU‑based inference pipelines for large multimodal diffusion models; build, optimize, and maintain serving infrastructure to deliver low‑latency predictions at large scale; collaborate with DevOps teams to containerize models, manage autoscaling, and ensure uptime SLAs.
Model Optimization & Fine‑Tuning: Leverage techniques such as quantization, pruning, and distillation to reduce latency and memory footprint without compromising quality; implement continuous fine‑tuning workflows to adapt models based on real‑world data and feedback.
Production MLOps: Design and maintain automated CI/CD pipelines for model deployment, versioning, and rollback; implement robust monitoring (latency, throughput, concept drift) and alerting for critical production systems.
Performance & Scaling: Explore cutting‑edge GPU acceleration frameworks (e.g., TensorRT, Triton, TorchServe) to continuously improve throughput and reduce costs.
Requirements
Technical Expertise: Proven experience deploying deep learning models on GPU‑based infrastructure (NVIDIA GPUs, CUDA, TensorRT, etc.); strong knowledge of containerization (Docker, Kubernetes) and microservice architectures for ML model serving; proficiency with Python and at least one deep learning framework (PyTorch, TensorFlow).
Model Optimization: Familiarity with compression techniques (quantization, pruning, distillation) for large‑scale models; experience profiling and optimizing model inference (batching, concurrency, hardware utilization).
Infrastructure: Hands‑on experience with ML pipeline orchestration (Airflow, Kubeflow, Argo) and automated CI/CD for ML; strong grasp of logging, monitoring, and alerting tools (Prometheus, Grafana, etc.) in distributed systems.
Domain Experience: Exposure to diffusion models, multimodal video generation, or large‑scale generative architectures; experience with distributed training frameworks (FSDP, DeepSpeed, Megatron‑LM) or HPC environments.
Benefits
Comprehensive medical, dental, and vision plans
401K with employer match
Commuter benefits
Catered lunch multiple days per week
Dinner stipend every night if you’re working late and want a bite!
Grubhub subscription
Health & Wellness perks (Talkspace, Kindbody, One Medical subscription, HealthAdvocate, Teladoc)
Multiple team offsites per year with team events every month
Generous PTO policy
Captions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Please note benefits apply to full‑time employees only.
Your role will require you to be in‐person at our NYC HQ (located in Union Square). We do not work with third‑party recruiting agencies, please do not contact us.
#J-18808-Ljbffr
This range is provided by Mirage. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $200,000.00/yr - $300,000.00/yr
About Mirage Mirage is the leading AI short‑form video company. We’re building full‑stack foundation models and products that redefine video creation, production and editing. Over 20 million creators and businesses use Mirage’s products to reach their full creative and commercial potential. We are a rapidly growing team of ambitious, experienced, and devoted engineers, researchers, designers, marketers, and operators based in NYC. As an early member of our team, you’ll have an opportunity to have an outsized impact on our products and our company’s culture.
About the role Captions is seeking a Machine Learning Engineer to partner closely with our Researchers and bring large‑scale multimodal video diffusion models into production. You’ll be responsible for optimizing and deploying state‑of‑the‑art generative models to deliver low‑latency, high‑throughput inference at scale, ensuring these innovations reach millions of creators worldwide.
Responsibilities
Inference & Deployment: Develop high‑performance GPU‑based inference pipelines for large multimodal diffusion models; build, optimize, and maintain serving infrastructure to deliver low‑latency predictions at large scale; collaborate with DevOps teams to containerize models, manage autoscaling, and ensure uptime SLAs.
Model Optimization & Fine‑Tuning: Leverage techniques such as quantization, pruning, and distillation to reduce latency and memory footprint without compromising quality; implement continuous fine‑tuning workflows to adapt models based on real‑world data and feedback.
Production MLOps: Design and maintain automated CI/CD pipelines for model deployment, versioning, and rollback; implement robust monitoring (latency, throughput, concept drift) and alerting for critical production systems.
Performance & Scaling: Explore cutting‑edge GPU acceleration frameworks (e.g., TensorRT, Triton, TorchServe) to continuously improve throughput and reduce costs.
Requirements
Technical Expertise: Proven experience deploying deep learning models on GPU‑based infrastructure (NVIDIA GPUs, CUDA, TensorRT, etc.); strong knowledge of containerization (Docker, Kubernetes) and microservice architectures for ML model serving; proficiency with Python and at least one deep learning framework (PyTorch, TensorFlow).
Model Optimization: Familiarity with compression techniques (quantization, pruning, distillation) for large‑scale models; experience profiling and optimizing model inference (batching, concurrency, hardware utilization).
Infrastructure: Hands‑on experience with ML pipeline orchestration (Airflow, Kubeflow, Argo) and automated CI/CD for ML; strong grasp of logging, monitoring, and alerting tools (Prometheus, Grafana, etc.) in distributed systems.
Domain Experience: Exposure to diffusion models, multimodal video generation, or large‑scale generative architectures; experience with distributed training frameworks (FSDP, DeepSpeed, Megatron‑LM) or HPC environments.
Benefits
Comprehensive medical, dental, and vision plans
401K with employer match
Commuter benefits
Catered lunch multiple days per week
Dinner stipend every night if you’re working late and want a bite!
Grubhub subscription
Health & Wellness perks (Talkspace, Kindbody, One Medical subscription, HealthAdvocate, Teladoc)
Multiple team offsites per year with team events every month
Generous PTO policy
Captions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Please note benefits apply to full‑time employees only.
Your role will require you to be in‐person at our NYC HQ (located in Union Square). We do not work with third‑party recruiting agencies, please do not contact us.
#J-18808-Ljbffr