Top Tech Jobs & Startup Jobs in Austin, TX

Review and assess domain-specific documents, provide structured feedback, and evaluate quality, accuracy, and relevance. The role requires strong analytical, written communication, document review, and attention-to-detail skills, along with proficiency in Excel, PowerPoint, and Word. Candidates must have a bachelor’s degree, at least two years of relevant professional experience, and be based in the United States, Europe, the UK, or Canada.

Builds a network of experienced management consultants for potential future AI evaluation projects. Responsibilities may include creating grading criteria for consulting deliverables, evaluating AI-generated or human-created work, providing evidence-based written feedback, and calibrating judgments with senior reviewers. Candidates should have management consulting experience and strong skills in structured problem-solving, market sizing, financial modeling, business-case development, and executive presentations.

Evaluate and improve clinical AI systems through grading-criteria development, dialogue evaluation, clinical reasoning annotation, output review, guideline authoring, and difficult-case writing. The role requires physicians to assess accuracy, safety, completeness, clinical reasoning, and escalation guidance without providing direct patient care. Work is assigned across shared clinical AI workstreams and requires at least 20 hours weekly.

Author point-in-time political reports and election forecasts, assess polling and market-implied probabilities, explain causal political and market impacts, and evaluate AI-generated analyses. Requires substantial live forecasting experience, strong polling literacy, probabilistic judgment, and disciplined analysis without hindsight or confidential information.

Forecast the timing and content of named sell-side equity analysts’ post-catalyst research publications. The role requires analyzing filings, earnings, transcripts, estimates, valuation, and ratings; documenting equity research workflows; and grading AI-generated analyses against expert standards using strict point-in-time reasoning without hindsight or confidential information.

Evaluate AI systems on personalized, real-world life tasks. Use extensive LLM experience to assess whether outputs are useful, realistic, safe, complete, and tailored to context, preferences, constraints, and tradeoffs. Provide detailed written judgments across areas such as food, health, productivity, careers, and learning, contributing to improvements in AI assistants and personal workflows. Expected commitment is 20–40 hours per week.

Seeking an expert senior software engineer with 10+ years of experience at top U.S. technology firms to improve foundational AI model capabilities on complex software engineering tasks. The role requires approximately 20 hours per week for a 2–3 week contract. Candidates must complete a 30-minute AI interview and a paid asynchronous software engineering exercise and project-instructions quiz. Compensation varies by geography and experience.

Build and evaluate MLOps and ML systems tasks for frontier AI training data. Responsibilities include designing technical challenges, writing solutions and evaluation rubrics, profiling and optimizing GPU workloads, debugging distributed systems, improving model performance, and supporting high-throughput LLM inference. The role requires production experience with ML infrastructure, serving systems, GPU accelerators, JAX or PyTorch, and strong technical communication.

Own the organization’s digital reputation across search engines, media, online platforms, and customer communities. Responsibilities include improving Page One visibility, developing SEO content, managing digital PR and earned media, monitoring reputation signals, responding to crises, building customer advocacy, tracking performance metrics, and adapting strategies for AI-powered search, GEO, and evolving algorithms.

Design insurance pricing, reserving, forecasting, and portfolio-risk scenarios for AI model training. Create actuarial analyses and reference answers, evaluate AI outputs for quantitative and methodological accuracy, identify unsupported assumptions and errors, and provide structured feedback. The role requires explaining actuarial methods and business implications to technical and nontechnical audiences while participating in calibration and onboarding sessions.