Top Tech Jobs & Startup Jobs in Los Angeles, CA

Leads Duck Creek Technologies’ Intelligence function, overseeing analytics engineering, business intelligence, reporting, dashboards, metrics, and insight generation. Builds scalable analytics platforms and governance standards, partners with product, AI, engineering, and business leaders, and translates complex data into actionable recommendations. Manages analytics teams and advances enterprise data-driven decision-making, operational performance, and product development.

Own the product vision, strategy, roadmap, and backlog while aligning US Air Force stakeholders, development teams, and end users. Prioritize features, make product decisions, create user stories and acceptance criteria, facilitate cross-functional collaboration, and communicate product goals to senior stakeholders. Apply Agile and Lean Product Management principles, user-centered design, research, and iterative feedback to guide products from concept through delivery.

Design and scale backend services, data pipelines, event-driven systems, and integrations supporting autonomous manufacturing operations. Build LLM agents and RAG workflows for supplier document analysis, manage PostgreSQL data models, integrate ERP and freight APIs, implement caching and rate limiting, and establish observability and incident response practices. The role requires transactional processing expertise, NLP/ML pipeline experience, and manufacturing supply chain domain knowledge.

Leads platform engineering strategy across Azure infrastructure, legacy Windows Server workloads, infrastructure as code, CI/CD, reliability, identity, security, cost optimization, and engineering tooling. Builds and develops a platform engineering team while overseeing tenant consolidation, modernization, observability, incident response, and developer self-service. Partners with Security and Compliance to support sensitive and regulated data environments.

Own the technical sales process for enterprise security, privacy, AppSec, and GRC buyers. Responsibilities include technical discovery, tailored demos, proofs of value, architecture discussions, security and data-handling objections, vendor reviews, and reusable demo and evaluation materials. Partner with Sales through signature while translating customer feedback into actionable improvements for Engineering.

Leads sales operations, coaches representatives and managers, manages KPIs and CRM processes, oversees training and hiring, supports financing and transactions, and drives accountability, productivity, and revenue growth across sales teams.

Leads the architecture, development, and maintenance of scalable batch and real-time data pipelines, cloud data platforms, ETL/ELT workflows, and ML data infrastructure. Establishes standards for data quality, observability, lineage, governance, security, and modeling. Partners with data scientists, engineers, DevOps, and business stakeholders; evaluates technologies, drives technical initiatives, mentors engineers, and supports production-grade APIs and machine learning pipelines.

Manage and develop a team of 8–12 Account Executives responsible for new business sales. Set goals, coach representatives, manage pipelines, forecast results, diagnose performance gaps, implement improvement plans, recruit and retain talent, and ensure teams meet or exceed sales targets. The role is fully remote and requires strong communication, analytical, organizational, leadership, and sales management skills.

Owns positioning, messaging, competitive intelligence, content, and sales enablement for Moby, Triple Whale’s AI product. The role translates complex AI capabilities into customer value, develops market differentiation, evaluates competing solutions, creates collateral and demonstrations, and serves as an AI evangelist. The manager collaborates closely with Product, Engineering, Sales, Customer Success, Growth, Brand, and executive leadership to improve product adoption and go-to-market effectiveness.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.