Member of Technical Staff - AI / ML Platform Engineer

Bernard•New York City, NY

5d•Onsite

About The Position

Bernard fixes appliance repair issues by ensuring the right part and diagnosis on the first visit, preventing repeat trips. Our AI analyzes calls, runs diagnostics, predicts necessary parts based on live inventory, and equips technicians with a plan. We are building the operating system for appliance repair from our New York City office and are experiencing rapid growth. Every new hire plays a crucial role in shaping the product, customer experience, and the company's future trajectory. The AI and Machine Learning Platform Engineer will be responsible for building Bernard's AI and ML platform. This includes the infrastructure for search, retrieval, training, evaluation, inference, and model deployment, as well as the models that translate operational data into informed decisions. The role encompasses both Diagnostics and Bernard's central intelligence system, which supports voice agents, chat agents, and future agent interfaces. This system will manage shared context, memory, knowledge, tools, policies, and learning loops. This is a comprehensive, hands-on systems role requiring work across the entire lifecycle from model creation to production infrastructure. The engineer will be accountable for platform reliability and enhancing how engineers develop and deploy software in an AI-assisted coding environment. The primary objective is to establish a rapid and reliable platform that empowers the entire team to create, assess, deploy, and enhance intelligent products, rather than focusing on isolated experiments.

Requirements

5+ years of professional software or machine learning engineering experience and have shipped production systems.
Strong in Python and Typescript and comfortable working across models, backend systems, data infrastructure, and cloud compute.
Understand modern search and ML systems, including retrieval, indexing, evaluation, serving, and the failure modes of deployed models.
Can reason clearly about distributed systems, performance, reliability, observability, and cost.
Care about engineering leverage and have strong opinions about how AI-assisted development should improve speed without lowering quality.
Can move between research, infrastructure, and product delivery based on what creates the most value.
Excited to build intelligence that spans diagnostics and customer experience, rather than being confined to a single model, modality, or product surface.
Care about measurable customer outcomes more than benchmark wins.

Nice To Haves

Experience with ranking, recommendations, vector or graph search, multimodal models, or real-time inference.
Experience building internal ML platforms, model gateways, evaluation systems, data platforms, or developer tooling.
Experience with GPUs, inference optimization, orchestration, CI/CD, infrastructure as code, or production incident response.
Experience using AI code generation tools deeply and building guardrails or workflows around them.

Responsibilities

Build and operate machine learning infrastructure for search, retrieval, indexing, training, evaluation, inference compute, model serving, and observability.
Create and improve models for diagnostics, failure-mode prediction, parts prediction, ranking, extraction, customer-support automation, and other product capabilities.
Build the common intelligence layer across voice and chat agents, including shared context, memory, retrieval, knowledge, tool use, policies, identity, and cross-channel continuity.
Design data and feedback pipelines across service history, manuals, equipment metadata, conversations, images, technician actions, and repair outcomes.
Own platform reliability across distributed services, data pipelines, search systems, and online inference, including monitoring, latency, capacity, failure recovery, and incident prevention.
Build evaluation systems that connect model and retrieval quality to real customer and repair outcomes across diagnostics, voice, chat, and customer-support workflows.
Turn conversations, agent actions, customer history, and human feedback into learning loops that continuously improve the unified brain across channels.
Improve developer experience for an AI-native engineering team through better environments, testing, CI/CD, observability, reusable platform primitives, and safe workflows for AI-generated code.
Partner with product engineers and domain experts to turn new models and platform capabilities into fast, explainable, production-ready experiences.
Make pragmatic architecture decisions across build versus buy, compute, storage, model providers, and open-source infrastructure.