Top Tech Jobs & Startup Jobs in NYC, NY

Qualify inbound leads and outbound to strategic prospects, triage against ICP, build the inbound playbook with marketing, use AI tools to research and personalize outreach, prioritize pipeline, and convert high-quality prospects into opportunities.

Own post-sale technical deployments for enterprise customers, configuring monitors, alerts, and integrations in Snowflake and Databricks environments. Write production Python, SQL, and API integrations, drive customer adoption and measurable value, expand usage across teams and use cases, and provide feedback to Product and Engineering. Help establish the forward-deployed engineering playbook while advising data engineering teams and executives.

Own a US enterprise sales territory focused on landing net-new logos among companies with 1,000–10,000 employees. Build outbound pipeline, run full-cycle deals, lead MEDDPICC-based discovery, collaborate with Solutions Architects on technical evaluations, and engage data and AI executives. The role also contributes customer insights to Product and Marketing, develops segment strategy and sales collateral, and helps build the growing enterprise sales team.

Own and expand a portfolio of 15–25 enterprise accounts, generating approximately $1.2M in annual expansion quota. Prospect within existing customers, identify new departments and use cases, build executive relationships, run business and quarterly reviews, and close complex 6–12 month enterprise deals. Partner with sales engineers, customer success, and leadership while creating account plans, developing pipeline, and presenting Monte Carlo’s data and AI reliability platform to technical and executive buyers.

Leads Monte Carlo’s Enterprise Americas sales team, overseeing forecasts, pipeline coverage, new-logo bookings, outbound prospecting, deal inspection, and enterprise sales coaching. The role requires developing a repeatable sales process, coaching reps through complex executive-level deals, recruiting enterprise account executives, and partnering with sales engineering, SDR, marketing, and customer success teams. Candidates should have prior enterprise SaaS sales leadership experience and expertise in disciplined methodologies such as MEDDPICC and value-based selling.

Own a 50–60-account Global 2000 territory, generate pipeline through outbound and partner channels, engage executive buyers, and close complex six- and seven-figure enterprise deals. Responsibilities include discovery, business value assessments, multi-threading, procurement and security navigation, account planning, and expanding initial wins across departments. The role also contributes competitive intelligence and helps shape Monte Carlo’s upmarket sales playbook.

Build and operate production-grade Python backend services, APIs, distributed systems, data pipelines, and agentic workflows for an AI observability platform. Own ambiguous projects from prototyping through architecture, testing, deployment, and ongoing operation. Collaborate with product, machine learning, and infrastructure teams, while contributing React front ends when needed. The role requires strong backend depth, distributed-systems expertise, cloud experience, and end-to-end product ownership.

Own technical sales cycles for strategic enterprise accounts, including discovery, solution design, demonstrations, proofs of value, architecture reviews, and technical close. Help customers deploy and observe AI agents across platforms such as Databricks and Snowflake. Build technical champions, navigate procurement and infosec reviews, and partner with Account Executives to drive revenue growth and expansion.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.