Applied AI Engineer at Mistral AI integrating AI products for clients, managing complex technical challenges while working in a collaborative environment.
About the role
- Staff Machine Learning Engineer at Adobe, leading technical efforts for scalable GenAI services across products like Photoshop and Lightroom. Collaborating closely with research and product teams for high-performance solutions.
Responsibilities
- Design, build, and maintain scalable, production-grade GenAI services in the cloud
- Lead architectural design and guide the development of optimized ML pipelines for cloud-based inference and integration
- Drive the bridge between research and product teams throughout the lifecycle—from prototyping to deployment
- Optimize model performance, GPU utilization, and service orchestration at scale
- Work collaboratively with PMs, TPMs, and engineering leads to build and implement the GenAI roadmap
- Define technical direction, conduct building reviews, and establish guidelines for reliability, scalability, and maintainability
- Own and improve CI/CD systems and monitoring pipelines for ML services
- Mentor engineers across ML and backend fields, encouraging a culture of technical excellence
- Provide tier-1 production support, ensuring service SLAs and customer satisfaction
Requirements
- MS or PhD in Computer Science or a related field, or equivalent experience in the industry.
- 8+ years' experience architecting cloud services and infrastructure for large scale use, reliability and performance
- 3+ years of experience with GenAI workloads—including fine-tuning and inference at scale
- Strong foundation in Transformer architecture, Diffusion models, CLIP, VAE, Encoder/Decoders
- Deep understanding of model serving, orchestration, and GPU resource management in distributed environments
- Knowledge of model optimization techniques (quantization, pruning, distillation, etc.)
- Expert-level coding and debugging skills in Python; familiarity with JavaScript/TypeScript is a plus
- Hands-on experience with Kubernetes, Docker, and ML-Ops platforms (e.g., MLflow, KServe, Triton)
- Familiarity with CUDA, Torch AOTinductor, and frameworks such as PyTorch, TensorFlow, or ONNX Runtime
- Proven track record of leading complex, high-stakes technical initiatives across teams
- Strong problem-solving skills and a mentality for developing well-tested, production-quality software
- Excellent communication and multi-functional collaboration skills.
Benefits
- Health insurance
- Retirement plans
- Flexible work arrangements
- Professional development
- Paid time off
- Bonuses
- Stock options
- Equipment allowances
Job title
Job type
Experience level
Salary
Degree requirement
Tech skills
Location requirements
Lead AI/ML Engineer on army enterprise team training and deploying models on cutting - edge AI technologies. Collaborate across teams to solve real - world challenges in the threat landscape.
AI Engineer building agentic systems and applying AI models for national security initiatives. Collaborating with teams to solve client challenges with cutting - edge AI solutions.
Machine Learning Scientist III at Expedia Group developing ML algorithms to enhance customer experience. Tackling complex problems in online travel for improved post - booking recommendations and service.
Senior Machine Learning Engineer architecting next - gen Agentic AI systems for enterprise workflows at Demandbase. Focused on multi - agent orchestration and LLM - powered reasoning systems.
AI/ML Intern supporting the design and deployment of AI and ML projects. Collaborating with senior leaders and engineers at Frontier Technology Inc.
Distinguished AI/ML Engineer leading technical development of agentic AI systems for Walmart Global Tech. Ensuring system reliability and operational excellence with advanced AI solutions.
Machine Learning Engineer at Red Hat focused on AI model optimization and collaboration with research teams. Developing software for LLM training and deployment in enterprise settings.
Machine Learning Engineer developing AI - driven fitness solutions at BHOUT. Analyzing performance data and building predictive models to coach users effectively.
Machine Learning Engineer position at RATP focuses on developing and optimizing Generative AI solutions. Engage with data science and machine learning within the company’s Data Factory.