AI Jobs in New York

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Lead multiple Mason pods and own delivery outcomes including scope, milestones, quality, and on-time execution. Translate ambiguous customer/internal requests into clear plans, acceptance criteria, and execution strategy. Set and enforce production-quality standards for Mason builds such as testing, monitoring, runbooks, documentation, and rollout plans. Serve as technical escalation for difficult problems involving auth/permissions, integrations, data modeling, reliability, and failure recovery. Establish and evolve team processes including scoping discipline, QA gates, review checklists, incident/postmortem loops, and continuous improvement. Drive prioritization and capacity planning across pods, identify the critical path, and remove blockers quickly. Partner with Delivery Leads and stakeholders to manage tradeoffs, timelines, and expectations, including client-facing escalations. Hire and build the team by defining roles, running interview loops, calibrating, closing candidates, and improving onboarding. Manage performance by setting expectations, delivering feedback, coaching growth, and handling underperformance clearly and fairly. Develop leaders within the Mason organization through mentoring, delegation, and fostering strong ownership at every level.

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As a Senior AI Engineer at Supernal, you will be responsible for shipping user-facing agent experiences end-to-end, from prototype through production and iteration based on real usage. You will architect and implement stateful agent systems including workflows, tool calling, memory, retrieval, and human-in-the-loop where needed. You will build voice features end-to-end that provide value, including realtime speech agents, voice UI/UX, prompt/audio routing, and guardrails for safe tool execution. You will build and own an evaluation harness that includes curated test sets, scenario suites, automated scoring, prompt/model/version tracking, and safe rollout patterns such as canary and A/B experimentation. You will design data and retrieval pipelines involving chunking, enrichment, metadata strategy, hybrid retrieval using vector, keyword, and structured filters, as well as re-ranking, caching, latency optimization, multi-tenant safety, and data isolation. You will integrate with and extend platform primitives including Django/DRF/ASGI services, async execution, queues, workflow orchestration, PostgreSQL with pgvector, Kubernetes deployments, autoscaling, and cost controls. Finally, you will establish engineering rigor for agents encompassing observability with traces, spans, structured logs, reliability patterns such as timeouts and retries, circuit breakers, graceful degradation, and security/privacy controls for data access and tool execution.

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The Senior Data Intelligence Engineer is responsible for building and maintaining high-fidelity dbt and SQL models that serve as the foundational data for complex, usage-based revenue models. They develop tools and permissions frameworks enabling 'Analyst Agents' to query data sources such as Athena, correlate Salesforce churn signals, and identify API latency issues. The engineer acts as the technical liaison with the Engineering/Infrastructure team to ensure data contracts are reliable and ready for autonomous agents. They partner with the Head of Data to ingest and transform thousands of hours of unstructured internal call audio into queryable insights for go-to-market teams using Deepgram’s own models. The role includes maintaining a culture focused on automating manual and repetitive SQL tasks through code and agent systems rather than legacy dashboards.

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As an Applied AI Engineer, responsibilities include building and shipping AI features end-to-end from model to system to user experience, designing and iterating on prompts, tools, memory, and agent workflows, turning raw model outputs into structured, reliable, and predictable behaviors, debugging issues across the full stack including model, orchestration, infrastructure, and UX, optimizing for latency, cost, and production reliability, developing lightweight evaluation frameworks to measure real-world performance, and working closely with product and engineering teams to translate ambiguous problems into working systems.

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Build and deploy AI agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Act as the primary technical partner for customers by leading regular demos, communicating progress, gathering feedback, and guiding solutions from concept to production. Configure and connect systems using APIs to handle authentication, data mapping, error handling, and integrate with CRMs, knowledge bases, and other enterprise tools. Set up telephony systems including SIP/CCaaS/PSTN routing, metadata passing, fallback configurations, and troubleshooting call quality. Write and refine prompts for LLM-driven agents, monitor performance, test iteratively, and ensure agents meet automation and containment targets. Translate customer requirements into actionable solutions and work consultatively to unblock challenges related to security, connectivity, or knowledge ingestion. Collaborate cross-functionally with product and engineering teams to escalate platform gaps, resolve technical issues, and independently drive leading client implementations.

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As a Senior Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio streaming and latency optimization integrated with speech providers. You will build and improve conversation intelligence systems that manage LLM layers, including prompt construction, context management, function calling, and dialogue management to create natural, actionable phone conversations. You will develop the action framework allowing configurable API calls with branching logic and runtime execution, supporting tasks like data lookup and ticket creation during calls. You'll manage knowledge ingestion, storage, and retrieval to enhance agent memory and learning over time. You will collaborate with designers to enable customers to create, configure, test, and deploy voice agents through intuitive product experiences. Additionally, you will help develop evaluation frameworks, analytics, call quality metrics, and monitoring instrumentation, and participate in on-call rotation duties.

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Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.

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You will be building innovative customer support AI products by defining AI-first product development through UI/UX, capabilities, and data models. Day-to-day responsibilities include taking ownership of challenging problems from idea to production, working closely with coding agents as a core part of your workflow, building tooling and feedback systems that enhance their effectiveness, and exercising engineering judgment to determine when outputs are ready to ship or need iteration. Senior engineers will own features end-to-end, while staff-level engineers will have opportunities to shape the technical direction of the team.

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Use machine learning expertise to create domain-relevant questions and review AI-generated responses for accuracy, rigor, and relevance to real-world physics research and practice.

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As a Software Engineer, Agents, you will build systems that make AI agents indispensable to legal professionals by designing environments and actions for agentic professional work, making model selection decisions, managing context windows, creating optimal tools, and developing evaluation harnesses for faster iteration loops to unlock new capabilities. You will partner with customers and product managers to understand legal workflows, design practical evaluations to capture what excellence means, and ship agents that effectively complete tasks. Additionally, you will optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and evaluation harness development. You will work with the model infrastructure team to design and implement infrastructure for low-latency agent execution, including caching strategies, parallel tool calls, or subagent patterns. Improving observability and instrumentation to profile agent behavior, identify bottlenecks, and drive optimization decisions is also part of the role. Staying current on new developments in agentic systems and applying those insights to product development is expected.