Senior AI/ML Platform Engineer — Production Infrastructure

Bernardhq; New York, Northern (NY, KY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Bernard is building the AI and ML platform behind its products, spanning search, retrieval, training, evaluation, inference, and model deployment. You will own the diagnostics and customer-support brain powering voice and chat agents with shared context, memory, and learning loops.

The role is hands-on, from model creation to production infrastructure, emphasizing reliability, observability, and rapid, safe AI-generated code. Based in New York City with a fast-paced, collaborative environment.

Qualifications

• 5+ years of professional software or ML engineering experience and shipped production systems.
• Strong in Python and TypeScript and comfortable 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.
• Able to 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.
• Able to 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.

Responsibilities

• Build and operate ML 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.

Skills

Python

Typescript

Distributed systems

ML engineering

Tools

CI/CD tooling

Infrastructure as Code

Job description

Bernard is building the AI and ML platform behind its products, spanning search, retrieval, training, evaluation, inference, and model deployment. You will own the diagnostics and customer-support brain powering voice and chat agents with shared context, memory, and learning loops.

The role is hands-on, from model creation to production infrastructure, emphasizing reliability, observability, and rapid, safe AI-generated code. Based in New York City with a fast-paced, collaborative environment.