Machine Learning Engineer
- Location: New York City
- Work Model: Onsite; 5 Days a Week
- Employment Type: Full-Time, Direct Hire
- US Citizenship or US Permanent Resident Status Required
Summary
Our client is seeking a Machine Learning Engineer to join their team! The ideal candidate will have hands-on experience in diffusion models, deep generative modeling, and deploying AI systems using TensorFlow or PyTorch. This role involves working on advanced projects with denoising diffusion probabilistic models (DDPMs), latent diffusion models (LDMs), and related architectures to build state-of-the-art generative AI applications. The Machine Learning Engineer will work closely with data engineers, research scientists, and software engineers to develop and deploy production-ready AI solutions.
Responsibilities
- Design, develop, and optimize diffusion models (DDPMs, LDMs) for tasks such as image generation, text-to-image synthesis, or noise-based denoising
- Implement and fine-tune deep learning models using PyTorch or TensorFlow for generative AI applications
- Develop scalable ML pipelines for training and inference using multi-GPU, TPU, or distributed computing environments
- Enhance model performance through optimization techniques such as quantization, pruning, distillation, and mixed-precision training
- Integrate diffusion models into production systems, including API endpoints, cloud-based inference, and real-time processing
- Collaborate with research teams to explore new architectures and improvements in generative AI
- Utilize cloud services (AWS, GCP, Azure) and MLOps tools (MLflow, Kubeflow, ONNX, TensorRT) for model deployment and monitoring
- Stay updated on advancements in generative modeling and apply innovative techniques in ongoing projects
Requirements
- Strong knowledge of denoising diffusion probabilistic models (DDPMs), stable diffusion, latent diffusion models (LDMs), or similar generative AI techniques
- Hands-on experience implementing diffusion models from research papers and deploying them in practical applications
- Proficiency in deep learning architectures (CNNs, VAEs, GANs, Transformers, or ResNets) for generative modeling
- Expertise in TensorFlow or PyTorch, including writing custom training loops and fine-tuning large models
- Experience with multi-GPU/TPU training, data parallelism, model parallelism, and distributed training frameworks
- Knowledge of model acceleration techniques (ONNX, TensorRT, quantization, mixed precision training, JIT compilation, XLA optimization)
- Strong proficiency in Python and experience with containerized deployment (Docker, Kubernetes, FastAPI, Flask)
- Experience with cloud services (AWS, GCP, or Azure) for training and deployment
- Familiarity with MLOps tools like MLflow, Kubeflow, or Weights & Biases
Education/Certification Requirements
- A Bachelors Degree in Computer Science, Software Engineering, or a related field
Preferred Requirements
- Experience with implementing state-of-the-art generative AI techniques in production environments
- Proven ability to collaborate with cross-functional teams, including data engineers and software developers
- An Advanced Degree (Masters or Ph.D.) in a relevant field is highly preferred
Other Duties
- Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities required of the employee for this job. Duties, responsibilities, and activities may change.
About Us
At Envision, we are dedicated to bridging the gap between exceptional talent and leading organizations nationwide. Our mission is to transform the workforce landscape into a seamless and efficient hiring experience for both candidates and employers. With a robust portfolio of services, including strategic talent consulting, direct hire, and temporary staffing solutions, we empower businesses to build dynamic teams that drive success.
Equal Opportunity Employer Statement
Envision is an equal-opportunity employer. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.