Top Tech Jobs & Startup Jobs in Austin, TX

Leads cross-functional technical programs across engineering, operations, product, customer support, and business development. Establishes scalable systems and processes, coordinates feature implementation, manages multiple concurrent projects, sets timelines, and drives teams toward deadlines. The role requires strong stakeholder communication, influence without authority, comfort with ambiguity, and the ability to balance competing priorities in a rapidly scaling AI-focused company.

Leads cross-functional initiatives spanning customer support, operations, product, engineering, and business development. Establishes scalable systems and processes, coordinates feature development, manages multiple projects, sets timelines, and drives teams toward deadlines. The role requires strong stakeholder communication, influence without authority, comfort with ambiguity, prioritization skills, and an interest in AI systems.

Conduct full-cycle recruiting for technical and non-technical roles, including sourcing, screening, scheduling, candidate closing, and offer coordination. Own searches from intake through close, source candidates creatively, partner directly with hiring managers, assess market fit, manage multiple searches with urgency, and improve recruiting processes. Technical recruiting experience is a plus, while strong writing, autonomy, and judgment are essential.

Own the full lifecycle marketing program for a contributor workforce, including onboarding, activation, engagement, retention, and reactivation. Lead email strategy, segmentation, deliverability, testing, copywriting, and performance analysis. Run paid marketing experiments, productize marketing playbooks, and apply UX sensibility to communications. Collaborate with product, design, operations, and growth teams on positioning, messaging, launches, and platform improvements.

Design scalable human-data collection systems for AI model post-training, including task frameworks, data workflows, quality assurance, evaluation datasets, and delivery pipelines. Partner with engineering and product teams to improve throughput and reliability while preserving data quality. Translate collection and quality patterns into insights that improve project design, client deliverables, and product development.

Oversee end-to-end product operations, coordinate with product and engineering teams, track performance metrics, resolve operational roadblocks, manage product communications, and use data-driven insights to improve delivery and operational efficiency.

Conduct hands-on research with frontier AI labs to evaluate model behavior, identify failure modes, and design interventions that improve reliability. Responsibilities include designing evaluations, building data pipelines and fine-tuning workflows, collaborating with internal and external stakeholders, and addressing challenges involving long-context reasoning, tool use, and real-time feedback loops.

Research data strategies for large language model development, including designing data slices to identify model failure modes, modeling annotator behavior, and optimizing instruction clarity and reward-signal reliability. Develop metrics and evaluation frameworks to assess dataset quality, diversity, alignment impact, and downstream model performance. Collaborate with AI research teams and test data-centric methodologies in production environments.

Design and govern reinforcement learning environments, including modular APIs, curricula, reward and termination schemas, quality standards, telemetry, and evaluation suites. Build robust simulations of real-world tasks, synthetic data generators, and multi-agent ecosystems while mitigating reward hacking, mode collapse, and exploitable loopholes. Partner with research and engineering teams to create deterministic, observable, scalable environments and maintain reproducible, high-quality training signals.

Design and build core fullstack systems powering Surge’s AI platform. Own frontend product decisions, backend architecture, and high-impact workflows for data collection, labeling, and evaluation. Ship scalable, reliable, intuitive tools while making autonomous technical decisions, collaborating with engineering and machine learning leaders, and maintaining high standards for usability, performance, and maintainability.