ML Infrastructure Engineer

Echo Neurotechnologies; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive salary

Stock options

Comprehensive benefits

401(k) matching contributions

Job summary

A pioneering tech startup in neurotechnology is seeking a Senior Machine Learning Infrastructure Engineer to design and scale critical infrastructure powering ML applications. This role involves creating robust data pipelines and optimizing modeling processes, essential for developing innovative solutions in brain-computer interface technology. The ideal candidate has a Bachelor's degree, extensive experience in Python, and significant expertise in data infrastructure. Competitive compensation and a collaborative environment offered.

Qualifications

• 5+ years of experience in software engineering or large-scale data infrastructure.
• Experience designing and maintaining high-throughput data pipelines.
• Ability to partner with research teams to productionize workflows.

Responsibilities

• Design and build cloud infrastructure for ML models.
• Implement robust ML platforms across the model lifecycle.
• Establish best practices for operational excellence in ML.

Skills

Python

Distributed training frameworks experience

Excellent communication skills

Collaboration skills

Education

Bachelor's degree in Computer Science or a related field

Tools

PyTorch

Kubernetes

Docker

Job description

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, 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

Preferred Qualifications

• Advanced degree (MS or PhD) in Computer Science, Electrical Engineering, or a related technical discipline

• Proficiency in C++, Go, CUDA, Rust, and/or Java

• Experience in data engineering and systems ML for time-series data

• Deep understanding of the fundamentals of distributed systems, including scalability, fault tolerance, monitoring, observability, scheduling, performance tuning, and resource management

• Experience with cloud-native environments and orchestration (Kubernetes, Docker, etc.)

• Experience scaling foundation-model training infrastructure or multi-cluster computing environments

What We Offer

• An opportunity to work on exciting, cutting-edge projects to transform patients’ lives in a highly collaborative work environment.

• Competitive compensation, including stock options.

• Comprehensive benefits package.

• 401(k) program with matching contributions.

Equal Opportunity Employer

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

Confidentiality

All applications will be treated confidentially. Applicants may be asked to sign an NDA after the initial stages of the interview process.