Top Tech Jobs & Startup Jobs

Leads LawZero’s independent Evaluations Team, defining evaluation strategy, building benchmarks, datasets, environments, tooling, and red-teaming programs for Scientist AI and frontier LLM guardrails. Oversees capability, safety, explainability, honesty, adversarial robustness, and causal-mechanism evaluations. Builds an 8–10-person team, scales evaluation infrastructure, communicates results through technical reports and publications, and represents LawZero with AI safety institutes, collaborators, funders, and policymakers.

Architect, implement, scale, and maintain petabyte-scale data processing platforms for frontier AI models. Build reliable, observable, cost-efficient pipelines with heterogeneous and GPU-accelerated workloads, including failure recovery and restartability. Ensure datasets and intermediate outputs are versioned, reproducible, traceable, and documented with datasheets. Partner with AI researchers to integrate datasets into training pipelines and guide sustainable technology decisions.

Develop and execute LawZero’s international middle-powers government partnership strategy. Build relationships across national ecosystems, advance AI safety and sovereign AI messaging, lead policy development, support summit planning, shape in-country communications, and prepare senior-level government engagements. The role requires independent work across multiple political systems, diplomatic judgment, strong relationship-building, communications expertise, and sufficient AI knowledge to engage research and safety communities.

Manage full-cycle technical recruiting for AI, machine learning, and AI safety teams. Responsibilities include sourcing niche and passive talent, market mapping, talent-pool development, candidate engagement, stakeholder collaboration, offer negotiation, candidate experience, and recruitment-process improvement. The role requires strong knowledge of AI research recruiting, bilingual German and English communication, and the ability to operate effectively in a fast-paced startup environment.

Develop and optimize distributed machine learning training and inference systems for very large models. Collaborate with research scientists, diagnose performance bottlenecks, profile and debug experiment code, build orchestration tools, optimize compute resources, and establish best practices for large-scale ML workflows.

Translate advanced AI research into scientifically accurate content for policymakers, funders, partners, media, and the public. Develop blogs, research summaries, white papers, newsletters, briefings, and stakeholder communications; support grant applications and technical research funding materials; and assist with conference, outreach, and partnership communications. The role requires strong AI and machine learning fluency, exceptional English writing, professional French proficiency, and at least five years of relevant experience.

Design and implement novel machine learning models for advanced AI safety research. Collaborate with mathematicians and research scientists, adapt and fine-tune frontier models, create experimental protocols and evaluation frameworks, analyze results, and optimize model performance with ML engineers. The role requires deep learning research experience, expertise with major ML libraries and distributed computing, strong Python development skills, and effective interdisciplinary communication.

Leads machine learning research and engineering teams focused on AI safety and the Scientist AI approach. Responsibilities include managing and mentoring researchers and engineers, hiring and onboarding technical talent, directing complex cross-functional projects, optimizing reproducible research workflows, advancing foundation-model research, and translating scientific concepts into large-scale engineering execution.

Develop and optimize machine learning research workflows for large-scale model training and inference. Collaborate with research scientists to implement novel models and simulated environments, build tools that improve computing efficiency, and establish best practices for ML development. The role requires advanced ML expertise, deep learning research experience, high-performance computing, cloud platforms, containerization, and data infrastructure familiarity.

Develop and scale web-scale data pipelines that transform raw text into high-quality machine learning datasets. Responsibilities include deduplication, model-based scoring, filtering, toxicity and PII removal, metadata extraction, dataset versioning, provenance tracking, compliance, leakage detection, quality monitoring, and researcher tooling. The role also involves acquiring and evaluating text corpora, collaborating with research, engineering, legal, and governance teams, and developing novel data transformations for AI training.