ML Infrastructure Engineer

ML Infrastructure Engineer COMPANY OVERVIEW Echo Neurotechnologies is an exciting new startup in the Brain-Computer Interface (BCI) space, driving innovation through advanced hardware engineering and AI solutions. Our mission is to deliver cutting-edge technologies that restore autonomy to people living with disabilities and improve their quality of life. TEAM CULTURE Join a small, dedicated team of knowledgeable and motivated professionals. Our early-stage environment offers the opportunity to take ownership of broad decisions with significant and long-lasting impact. We emphasize continuous learning and growth, fostering cross-functional collaboration where your contributions are vital to our success. JOB SUMMARY We are seeking a Senior Machine Learning Infrastructure Engineer to join our team. The person who fills this role will design, build, and scale infrastructure to power massive-scale data, modeling, and analysis platforms, playing a critical role in shaping a high-performance, production-grade ML ecosystem to support rapid experimentation with diverse datasets spanning neural signals, behavior, and more. This person will have significant ownership over the ML R&D platform, working closely with domain experts to architect new cloud infrastructure, data pipelines, and modeling flows. The work will ultimately enable the development of cutting-edge models for neuroscientific discovery and neural decoding, empowering brain-computer interface technology to improve the lives of patients living with severe neurological conditions. KEY RESPONSIBILITIES - Create flexible and performant ML infrastructure - Design and build systems ML cloud infrastructure to enable massive-scale modeling and analytics - Support diverse model exploration, hyperparameter optimization, pretraining, fine-tuning, and evaluation processes - Design and optimize scalable distributed training pipelines, with support for features such model sharding, cross-GPU communication, and real-time training monitoring - Create, operate, and maintain robust ML platforms and services across the model lifecycle - Make informed architecture decisions that balance performance, cost, reliability, and scalability - Build diverse and scalable data platforms - Design, build, and optimize massive-scale databases and data pipelines for scalable, flexible, and reliable data access - Explore research-driven, tailored data solutions using existing and simulated data, comparing performance and efficiency across solutions for typical data-access patterns - Create infrastructure and pipelines for ingesting internal and external datasets with varied shapes, formats, and associated metadata - Design and assess custom data formats for efficient storage and slicing of high-dimensional time-series data - Enable efficient data movement, preprocessing, and artifact management for data lineage and modeling reproducibility - Meet company standards for delivered solutions - Establish best practices for reliability, observability, reproducibility, and operational excellence across the ML ecosystem - Make informed and collaborative decisions with domain experts across the software & ML teams - Foster visibility and reproducibility within the company by maintaining extensive documentation of design decisions, evaluations of viable alternatives for selected solutions, pipeline assessments, etc. - Support ML R&D operations while preparing for eventual incorporation into product pipelines REQUIRED QUALIFICATIONS - Bachelor's degree in Computer Science, Electrical Engineering, or a related technical discipline - 5+ years of industry experience in software engineering, large-scale data infrastructure, or systems ML - Extensive proficiency in Python - Familiarity with PyTorch - Experience designing, building, and maintaining high-throughput data pipelines for large and diverse datasets - Experience working with distributed-training frameworks (e.g. FSDP, DeepSpeed, Mega ... (truncated, view full listing at source)