Restorative Neurotechnologies

Procurement

• Develop and manage supplier relationships for Echo across the product life cycle, starting from design and development through commercial production.
• Collaborate with R&D engineers, cross-functional core team and suppliers during new product development process to develop critical medical devices, components, materials or other based on product and company need.
• Engage and utilize processes to coordinate New Product Development (NPD) initiatives including but not limited to ensure quality, cost, delivery and risk reduction activities with suppliers.
• Conduct Make vs Buy analysis, supplier selection, develop sourcing strategies, negotiate agreements (i.e., NDA, Development, Supply), PO management and manage supplier relationships to deliver predictable supply & competitiveness.
• Provide Project Mgmt. for Sourcing deliverables, working with overall project managers, communication with cross-functional groups and suppliers to track performance and highlight risks.
• Facilitate workshops with both internal partners and suppliers to utilize DFx strategies and tools (manufacturability, supply chain, etc.) along with component plans to drive quality and reliability of parts.
• Responsible for the identification, reduction and mitigation of risk, improving timelines and developing lasting relationships that are critical to meeting business and technical challenges.
• Track supplied component quality, supply risk, material cost, response / lead time for new products.
• Partner with suppliers to develop launch plans ensuring materials, labor and capacity is in place to meet demand.
• Develop methodology and manage the SRM (supplier relationship management) operating system for on-going supply working with supply partners (i.e., agreements, business reviews, technical meetings, etc.)
• Drive continuous improvement activities with suppliers

Senior Strategic Sourcing Manager

Infrastructure Engineer

• 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 as 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

ML Infrastructure Engineer

Research Engineer

• Design and assess varying model architectures and modeling strategies to maximize the accuracy and robustness of models for decoding speech from brain activity
• Explore the use of state-of-the-art speech features and representations within neural-decoding pipelines, informed by the fields of speech science and functional neurophysiology
• Develop pipelines for synthesizing personalizable and naturalistic speech from text and from brain activity
• Build algorithms to analyze intact and impaired speech signals to detect biomarkers related to underlying diseases and disabilities
• Work collaboratively within a small team to develop models, design R&D workflows, and translate scientific findings into product implementations
• Contribute to best practices for reliability, observability, reproducibility, and scientific rigor across the R&D ecosystem
• Maintain versioned, clear, and highly documented code, analysis pipelines, and results for maximum interpretability and reproducibility

ML Research Engineer, Speech

SDET

• Design, implement, and maintain integration tests for cloud, desktop, and mobile applications.
• Build automated test suites to verify end-to-end workflows and system behaviors.
• Drive the QA process for new software releases, including planning, test execution, defect triage, and release readiness assessments.
• Define acceptance criteria and partner with engineering and product teams to ensure alignment.
• Ensure testing practices align with relevant medical software standards and regulations.
• Document test plans, protocols, results, and verification evidence.

Software Test Engineer

Engineering Manager

• Introduce and foster engineering best practices that emphasize code quality, modularity, usability, and long-term flexibility.
• Mentor and develop engineers through regular feedback, coaching, and technical guidance, including people-management responsibilities.
• Design and implement key modules across the server-side product and its integration points.
• Partner closely with Quality and Regulatory teams to produce high-quality technical documentation.
• Translate validated research concepts into robust, production-quality software.

Software Engineering Manager

Platform Engineer

• Establish and maintain secure cloud environments, ensuring strong isolation and compliance with regulatory requirements.
• Build infrastructure-as-code (IaC) environments that are reproducible, extensible, and easy to automate.
• Design and implement integration tests and automated workflows that validate cross-system functionality.
• Monitor system health, reliability, and performance, and introduce improvements to stability and scalability.
• Support and deploy tools used internally during development, including for software and hardware applications.