Sr ML Engineer - Distributed Systems

We are NinjaTech AI: a new paradigm shift in human to machine conversational AI company focused on making people at work more productive. We use generative AI and our own LLM models and we are based out of Palo Alto, CA. The company is founded former Google & Meta executives/entrepreneurs with 30+ years of combined experience.

We are looking for a Machine Learning (ML) Engineer to help us create artificial intelligence products. Machine Learning Engineer responsibilities include creating machine learning models and retraining systems. To do this job successfully, you need exceptional skills in statistics and programming. If you also have knowledge of data science and software engineering, we'd like to meet you. Your ultimate goal will be to shape and build efficient self-learning applications.

Responsibilities

• Propose and Develop distributed systems to scale our ML services (Training & Inference)
• Apply ML and optimization technique to production systems to scale and improve performance of Natural Language systems in production
• Develop ML and optimization models in a full lifecycle, from offline training to online serving and monitoring.
• Collaborate with teams across the company to define ML roadmap.

• Incorporate efficiency metrics into our day to day compute and alleviate performance bottlenecks
• Design and implement infrastructure for orchestrating end to end machine learning lifecycles
• Deploy scalable systems on major CSPs (Azure, AWS, GCP, etc)
• Set up processes to monitor and continually improve efficiency, accuracy and performance of models
• Software development including algorithm implementation, optimization, performance profiling, integration to production systems, testing and documentation

Requirements

• BS/MS or PhD in CS, Computer Engineering, Networking, ML, AI, Engineering or equivalent
• Proven experience as a Machine Learning Engineer or similar role
• Deep understanding of distributed systems for ML applications
• Demonstrated ability to apply DDP and MDP techniques for ML models
• Working knowledge of cluster management and using distributed frameworks for messaging, workload orchestration, storage, etc such as Apache Kafka, Slurm, Zookeeper, Redis, Scala, MPI, OpenMP
• Working knowledge of designing distributed autoscaling systems on at least one cloud service provider (AWS, GCP, Azure,etc)
• 3+ years of designing and deploying large distributed optimized ML serving infra in a production environment
• Ability to code and iterate independently on top of data infrastructure tools like Python, Spark, Jupyter notebooks, standard ML libraries, etc.
• Take pride in taking ownership and driving projects to business impact.
• Experience with the industry application of optimization and/or reinforcement learning is a big plus.
• Data analytics and data engineering experience is a plus.Ability to work with technical and non-technical teams.
• Master's degree or equivalent work experience in Computer Science, Mathematics, Engineering, Operation Research, or a closely related field.

Benefits

Full benefits

• Health Care Plan (Medical, Dental & Vision)
• Paid Time Off (Vacation, Sick & Public Holidays)
• Free Food & Snacks
• Stock Option Plan