Jobs at Source Direct

Build and operate production AI platform capabilities, including agentic and multi-agent systems, evaluation and monitoring frameworks, fine-tuning workflows, inference optimization, safety controls, APIs, and rollback mechanisms. Partner with data and infrastructure teams to deliver reliable, scalable AI services while balancing latency, cost, GPU utilization, and operational risk. Senior engineers also mentor teammates, review designs, and raise engineering standards.

Own the end-to-end hardware lifecycle for private AI and data infrastructure, including architecture, component and silicon selection, system integration, validation, certification, procurement, manufacturing, and field reliability. Lead decisions across compute, GPUs, memory, storage, networking, thermal, and power systems while balancing performance, security, reliability, manufacturability, supply-chain risk, and total cost. Establish secure hardware foundations, manage vendors and contract manufacturers, and drive platform roadmaps from prototype through production and deployment.

Owns end-to-end customer deployments of AI and edge systems, including hardware, networking, sensors, data flows, integrations, and production inference. The role requires diagnosing cross-stack issues, building reusable deployment patterns, collaborating with customer and partner engineering teams, and translating field learnings into product improvements. It involves 25–50% travel and hands-on work across infrastructure, security, containerized software, and operational environments.

Build and operate production data and streaming systems across edge, hybrid, OT, IoT, and enterprise environments. Responsibilities include developing connectors, real-time event-driven pipelines, data contracts, schemas, validation, lineage, observability, telemetry, federated query patterns, and reliable integration platforms supporting analytics and AI. The role requires strong ownership, system design, documentation, incident readiness, and collaboration with engineering and customer-facing teams.

Own fleet-scale infrastructure and SRE architecture for an edge platform supporting thousands of devices. Lead reliability, scalability, secure lifecycle management, observability, incident response, upgradeability, and operational standards. Serve as technical owner during high-severity incidents, drive root-cause remediation, apply AI to operations, and coordinate cross-domain initiatives spanning software, hardware, networking, security, data, and AI runtime. Mentor senior engineers and provide architectural leadership across the organization.

Develops scalable deployment patterns, playbooks, success frameworks, and KPIs that improve customer ROI, adoption, repeatability, and time-to-value. Advises customer and partner executives, translates field insights into delivery systems and requirements, and creates reference materials such as use cases, customer stories, blogs, and white papers. Partners across engineering, product, commercial, and deployment teams while using AI tools to accelerate documentation and operational improvement. Requires approximately 25–50% travel.

Advocate for open-source cloud networking and AI infrastructure projects through technical content, conferences, workshops, community engagement, and customer collaboration. The role advises prospects and users, develops best practices, gathers ecosystem feedback, and partners with product and engineering teams on roadmap decisions. It requires software engineering experience, deep knowledge of Kubernetes, service mesh, cloud networking, and agentic AI, plus an established technical presence across open-source communities and social platforms.

Manage a portfolio of 20–25 technical SaaS customers across onboarding, adoption, renewals, and expansion. Build success plans, connect product usage to business outcomes, advise customers on QA and automated testing, engage technical and executive stakeholders, manage risks and escalations, and partner with Sales, Product, and Engineering. Own renewal and expansion forecasting while delivering net and gross dollar retention targets.

Lead the strategy and execution of an integrated demand-generation engine for a B2B SaaS company. Own SEO, paid acquisition, website conversion, lifecycle marketing, analytics, attribution, content, agency relationships, and budget allocation. Build scalable marketing programs that convert PLG users, generate enterprise pipeline, and connect open-source adoption to revenue. Partner with Product, Sales, Design, and executives on positioning, campaigns, KPIs, and growth planning while mentoring future team members.

Build and operate core data infrastructure for edge and hybrid environments, including ingestion, OT/IoT integrations, real-time streaming, data contracts, schemas, validation, lineage, observability, telemetry, and scalable query access. The role requires production ownership, reliability practices, customer integration, and collaboration across engineering and customer-facing teams. Candidates should have 6+ years of experience with production data, streaming, or integration systems and proficiency in Python and/or Go.

Maintain and support platform environments across the software development lifecycle. Configure monitoring, alerting, and self-healing solutions; troubleshoot application and infrastructure issues with developers and testers; maintain technical documentation; design scalable architectural solutions; and mentor junior engineers. The role requires cloud, Kubernetes, operating system, database, scripting, configuration management, .NET, and AI/ML automation expertise.

Build and own the company’s iOS mobile application from the ground up, with Android planned next. Develop mobile UX, integrate authentication, payments, databases, APIs, and existing AI technology, and contribute across the backend when needed. Translate designs into polished production code, ship rapidly, iterate using user feedback and data, and establish mobile engineering practices. Collaborate closely with engineering, product, and design in a fast-paced, in-office startup environment.

Design and scale a feature flagging and A/B testing platform, including high-throughput event ingestion, warehouse-native analytics, real-time feature flag delivery, multi-database SQL support, and distributed systems infrastructure. The role involves full-stack development, architecture, performance optimization, reliability engineering, and technical product influence within a remote startup team.

Build and scale a permitting platform using React, Remix, TypeScript, Prisma, PostgreSQL, and AWS. Collaborate with customers and teammates, shape product direction and architecture, ship frequently, and establish engineering practices. The role emphasizes product-minded development, user empathy, clear communication, rapid delivery, and experimentation with AI-assisted workflows. Candidates should have at least two years of professional full-stack product development experience and be based on the West Coast, preferably in the San Francisco Bay Area.

Build and maintain scalable UI, API, and backend test automation using Python, Robot Framework, Playwright, and Selenium. Integrate testing into Jenkins and CI/CD pipelines, develop Page Object Model scenarios, analyze quality metrics, and improve software reliability. Collaborate with engineering, architecture, product, and UX teams; mentor junior QA engineers; and use AI-assisted tools safely for test generation and maintenance. The role is remote and requires no regular travel.

Own supply strategy and operations for a multi-tier delivery service provider network. Build and manage DSP relationships, negotiate contracts and SLAs, source and onboard partners, establish performance scorecards, and improve delivery KPIs. Analyze operational data, lead cross-functional issue resolution, and create scalable playbooks for onboarding, incidents, and capacity surges.

Designs and owns production machine learning systems for game development and live operations. Responsibilities include querying large-scale data warehouses, exploratory analysis, statistical testing, feature engineering, experimentation, model deployment in cloud environments, and collaboration with engineering and product teams. The role also involves mentoring junior data scientists, communicating insights to technical and non-technical stakeholders, and promoting data-driven decision-making.

Build and own full-stack software and AI-powered product features across frontend, backend, agentic infrastructure, and deployment layers. Develop React and TypeScript applications, maintain Bun and Elysia backend architecture, expand LangChain-based tooling, and collaborate with founders, ML specialists, domain experts, and customers. Translate complex real-world problems into trustworthy product solutions while independently delivering features and major improvements in a fast-paced, zero-to-one startup environment.

Own the post-sale customer lifecycle for enterprise software customers by driving adoption, value realization, retention, and growth. Build executive relationships, develop customer success plans, coordinate cross-functional resources, resolve complex issues, communicate product value, and support renewals through metrics gathering, pricing, quoting, negotiation, and closure.

Build and maintain revenue operations data pipelines, dbt models, integrations, and GTM systems across Salesforce, Marketo, warehouses, and analytics platforms. Diagnose synchronization and schema issues, ensure data integrity, translate ambiguous business needs into data solutions, test end-to-end workflows, and collaborate with Sales, Marketing, Customer Success, Finance, and Engineering. The role also involves experimentation with AI tools, technical documentation, and managing multiple projects independently.