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About the Role

We are seeking a Senior Machine Learning Infrastructure Engineer to design, build, and scale infrastructure for massive-scale data, modeling, and analysis platforms. This role is critical in shaping a high-performance, production-grade ML ecosystem to support rapid experimentation with diverse datasets. You will have significant ownership over the ML R&D platform, working closely with domain experts to architect cloud infrastructure, data pipelines, and modeling flows, ultimately enabling cutting-edge models for neuroscientific discovery and neural decoding.

Responsibilities

Create flexible and performant ML infrastructure
Design and build ML cloud infrastructure for 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 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 balancing 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
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 software & ML teams
Foster visibility and reproducibility by maintaining extensive documentation of design decisions, evaluations, and pipeline assessments
Support ML R&D operations while preparing for eventual incorporation into product pipelines

Requirements

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, Megatron-LM, Ray, etc.)
Experience building or optimizing ML training pipelines for transformers or other large neural-network models
Demonstrated ability to partner closely with research and modeling teams to productionize workflows
Excellent communication and collaboration skills to work effectively on cross-functional and interdisciplinary teams
Experience having technical ownership over at least one successfully implemented collaborative project
Advanced degree (MS or PhD) in Computer Science, Electrical Engineering, or a related technical discipline (Preferred)
Proficiency in C++, Go, CUDA, Rust, and/or Java (Preferred)
Experience in data engineering and systems ML for time-series data (Preferred)
Deep understanding of the fundamentals of distributed systems, including scalability, fault tolerance, monitoring, observability, scheduling, performance tuning, and resource management (Preferred)
Experience with cloud-native environments and orchestration (Kubernetes, Docker, etc.) (Preferred)
Experience scaling foundation-model training infrastructure or multi-cluster computing environments (Preferred)

Skills

Python
PyTorch
Distributed-training frameworks (FSDP, DeepSpeed, Megatron-LM, Ray)
ML training pipelines for transformers or large neural-network models
C++
Go
CUDA
Rust
Java
Data engineering
Systems ML for time-series data
Distributed systems fundamentals
Cloud-native environments
Orchestration (Kubernetes, Docker)

Location

Remote

Work Type

Full-time

Experience Level

Senior
5+ years of industry experience

Education Level

Bachelor's degree in Computer Science, Electrical Engineering, or a related technical discipline
Advanced degree (MS or PhD) in Computer Science, Electrical Engineering, or a related technical discipline (Preferred)

Benefits

Competitive compensation, including stock options
Comprehensive benefits package
401(k) program with matching contributions

About the Company

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.
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.

Equal Opportunity

Echo Neurotechnologies is an Equal Opportunity Employer (EOE). We celebrate diversity and are committed to creating an inclusive environment for all employees.