AI Jobs in New York

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The Platform Engineer within Forward Deployed Engineering (FDE) provides hands-on leverage by embedding with customer-tagged FDE pods to support generalization and contribute directly to architecture, product shaping, refactoring, and implementation. The role involves translating repeated cross-customer signals into platform bets by creating clear hypotheses with success criteria and validation plans. It includes raising engineering standards through tooling, mentorship, high-signal code review, pairing, and building lightweight developer tools to promote good architecture, readability, and correctness across FDE. The engineer collaborates closely with cross-functional platform teams including B2B Product, customer-tagged FDEs, ops, and business partners to deliver appropriate products and platform capabilities. Additionally, the role may lead complex platform capabilities end-to-end, acting as the direct responsible individual (DRI) from requirements through implementation, making key tradeoffs explicit, and involving customer pods early to maintain grounding in real deployments.

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Create functional black box tests for large codebases in various source languages. Create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms. Monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards. Leverage LLMs (Roo Code, Claude) to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Lead the deployment, performance, and reliability of AI agents operating in live, high-stakes healthcare environments. Architect and scale full-stack systems integrating with EHRs, legacy healthcare platforms, and real-time voice infrastructure. Drive technical strategy for customer-facing solutions, collaborating with product and engineering to translate client needs into scalable architecture. Mentor engineers and set standards for code quality, testing, security, and HIPAA-compliant development. Shape product roadmap by identifying systemic challenges in customer workflows and proposing high-leverage technical solutions.

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Build and ship production code including integrations, AI agent configurations, and platform logic based on customer workflows and scheduling rules. Own implementations end-to-end by creating and managing technical project plans from scoping through go-live, coordinating internal resources, delegating workstreams, and driving timelines to completion. Architect high-leverage systems and playbooks to transform complex implementations into repeatable, scalable infrastructure. Serve as the primary technical point of contact for customer teams, leading discovery sessions, resolving integration issues, and building trusted advisor relationships. Monitor AI agent performance at scale, diagnose failure modes, and iteratively improve behavior to enhance accuracy, reliability, and patient experience. Drive account growth post-launch by deploying new features, identifying upsell opportunities, and proactively solving emerging pain points. Translate customer feedback and field observations into actionable requirements to shape the product roadmap.

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New grad engineers will help build the systems that power Giga's AI agents by working across the backend, including data pipelines and integrations to agent infrastructure. They will ship features alongside experienced engineers, contribute to projects like building features for the AI assistant (including charts, alerts, natural language queries, and managing platform resources), developing log visualization tools, adding dynamic time-based knowledge updates, and implementing agent memory for context retention across sessions. They will be paired with senior engineers on larger initiatives while owning smaller projects end-to-end as they ramp up. Engineers are expected to ramp up quickly, take ownership of their work, and operate with increasing independence as they learn the codebase.

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As a senior backend infrastructure engineer at Giga, you will build the platform that powers AI agents, creating systems, tools, and abstractions to improve productivity and platform reliability. Your responsibilities include developing controlled deployment systems with traffic scaling, scheduling, and pass rate thresholds, building observability infrastructure such as instrumentation, logging, and monitoring to understand production behavior, constructing data pipelines and storage for training data, analytics, and agent memory, as well as creating developer tooling like internal CLIs, testing frameworks, and automation to reduce engineering team friction. You will own these critical systems end-to-end and make decisions impacting the reliability and velocity of the entire engineering team.

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Software engineers at Giga work on building the systems that power AI agents, including backend tasks such as data pipelines, integrations, and agent infrastructure. They contribute to projects across the technology stack, including building features for an AI assistant like charts and alerts in Slack, natural language queries, log visualization with filters, timestamps, and frequency charts for agent behavior visibility, adding dynamic time-based knowledge that auto-updates from sources, and developing agent memory for conversation awareness and context retention across sessions. Early-career engineers are paired with senior engineers on larger initiatives while owning smaller projects end-to-end as they ramp up, with expectations to take ownership of their work and operate with increasing independence.

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As the Technical Lead for Developer Experience at The Browser Company, you will design and lead the creation of an internal SaaS-like platform that enables engineers and non-engineers to describe what they want and have the system realize the rest. You will partner with AI and infrastructure teams to integrate existing AI coding tools, virtualized environments, continuous integration, AI code reviewers, evaluation tools, and prompt and model optimization into a cohesive, opinionated product that teams enjoy using. You will define success metrics for this platform and iterate rapidly based on usage and engineer feedback. Your goal is to transform the development experience stack into a lever to enable every engineer and product manager to deliver 10 times more impact.

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The Senior Backend Engineer will build the infrastructure and systems that power AI agents, working across the stack from data pipelines and analytics to real-time agent systems. They will ship features that directly impact customer operations, owning problems end-to-end by figuring out the right approach, building it, and iterating based on learnings. Responsibilities include migrating the analytics service to ClickHouse to scale growth, improving hallucination detection systems, and scaling, stabilizing, and adding features to the browser use agent. The engineer is expected to make sound technical decisions, ship reliably, and help set the bar for code quality and system design, with significant autonomy and influence over product development.

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As a Senior ML Operations Engineer at Eight Sleep, you will pioneer cutting-edge ML technologies and integrate them into products and processes for health monitoring. You will own the design and operation of robust ML infrastructure by building scalable data, model, and deployment pipelines to ensure reliable model delivery to production. Your role involves partnering cross-functionally with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales across Pods globally. You will optimize ML systems for cost-effectiveness, scalability, and high performance by managing compute, storage, and deployment resources during training and inference. Additionally, you will develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment, and maintain clear and direct communication within a remote work environment.