Founding AI Engineer
The Founding AI Engineer is responsible for building end-to-end training pipelines covering data, training, evaluation, and inference. They design new model architectures or adapt open-source frontier models, fine-tune models using state-of-the-art methods such as LoRA/QLoRA, SFT, DPO, and distillation. They architect scalable inference systems using technologies like vLLM, TensorRT-LLM, and DeepSpeed, and build data systems for high-quality synthetic and real-world training data. The engineer develops alignment, safety, and guardrail strategies and designs evaluation frameworks across performance, robustness, safety, and bias. They own deployment responsibilities including GPU optimization, latency reduction, and scaling policies. Additionally, they shape early product direction, experiment with new use cases, and build AI-powered experiences from zero, exploring frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal models. They take ownership of end-to-end problem solving, prototype, test, iterate, ship developments, and work with a mindset that values speed, discipline, humility, curiosity, and high standards within a founding team context.
Founding AI/ML Research Engineer
Build end-to-end training pipelines including data preparation, training, evaluation, and inference; design new model architectures or adapt existing open-source frontier models; fine-tune models using state-of-the-art methods such as LoRA/QLoRA, SFT, DPO, and distillation; architect scalable inference systems using technologies like vLLM, TensorRT-LLM, and DeepSpeed; build data systems for high-quality synthetic and real-world training data; develop alignment, safety, and guardrail strategies; design evaluation frameworks covering performance, robustness, safety, and bias; own deployment tasks focusing on GPU optimization, latency reduction, and scaling policies; shape early product direction, experiment with new use cases, and build AI-powered experiences from zero; explore frontier techniques including retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal models.
Founding Machine Learning Engineer
The Founding Machine Learning Engineer is responsible for shaping the core technical direction of the project including model selection, training strategy, infrastructure, and long-term architecture. The role involves building end-to-end training pipelines from data acquisition through training, evaluation, and inference. The engineer will design new model architectures or adapt existing open-source frontier models, fine-tune models using state-of-the-art methods, and architect scalable inference systems utilizing frameworks such as vLLM, TensorRT-LLM, and DeepSpeed. Responsibilities also include building data systems for producing high-quality synthetic and real-world training data, developing alignment, safety, and guardrail strategies, and designing evaluation frameworks that assess performance, robustness, safety, and bias. The engineer will own deployment processes focusing on GPU optimization, latency reduction, and scaling policies. Additionally, the role involves shaping early product direction, experimenting with new use cases, and building AI-powered experiences from scratch, exploring frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal models.
First-Line Supervisors of Food Preparation and Serving Workers - AI Trainer (Contract)
The responsibilities include evaluating what AI models produce related to the field of food preparation and serving work, assessing content related to the field of work, delivering clear and structured feedback to improve the AI model's understanding of workplace tasks and language, developing prompts for AI models that reflect the field, and evaluating AI responses. The work is performed remotely and asynchronously with flexible hours, and involves leveraging professional experience in food preparation and serving supervision to train AI models.
Member of Technical Staff - ML Research Engineer; Multi-Modal - Audio
Invent and prototype new model architectures that optimize inference speed, including on edge devices; build and maintain evaluation suites for multimodal performance across a range of public and internal tasks; collaborate with the data and infrastructure teams to build scalable pipelines for ingesting and preprocessing large audio datasets; work with the infrastructure team to optimize model training across large-scale GPU clusters; contribute to publications, internal research documents, and thought leadership within the team and the broader ML community; collaborate with the applied research and business teams on client-specific use cases.
Software Engineer, Evaluation Frontend
As an Evaluation Frontend Software Engineer, you will design tools and visualizations that enable researchers and engineers to compare and analyse hundreds of model evaluations, including both data visualization tools and statistical tools to extract signal from noisy data. You will develop an understanding of the relative merits and limitations of each model evaluation and suggest new facets of model evaluation. Your work will involve collaborating closely with cross-functional teams, including researchers and engineers, to surface necessary insights for model development.
Freelance Cybersecurity Analyst - AI Trainer
Analyze and investigate simulated security alerts and incidents across endpoints, identities, and cloud environments. Conduct proactive threat hunting using KQL or similar query languages to identify hidden vulnerabilities and emerging threats that automated systems may miss. Assess the accuracy and depth of AI-generated security incident reports and threat analyses. Review, validate, and improve the model’s understanding of Microsoft Defender products and SOC workflows. Provide expert feedback on AI performance in identifying and classifying cybersecurity threats.
Finance Platform Engineer
Use proprietary software applications to provide input and labels on defined projects. Support and ensure the delivery of high-quality curated data. Contribute to the training of new tasks by working closely with the technical staff to develop and implement cutting-edge initiatives and technologies. Interact with technical staff to improve the design of efficient annotation tools. Choose problems from economics fields that align with expertise, focusing on macroeconomics, microeconomics, and behavioral economics. Regularly interpret, analyze, and execute tasks based on given instructions. Provide services including labeling and annotating data in text, voice, and video formats to support AI model training, sometimes involving recording audio or video sessions.
Data Engineer – Spark Specialist
Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help make customers successful. Scope and co-develop production-level data science projects with customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.
Product Marketing Manager, Developer Platform
As Product Marketing Manager, you drive go-to-market strategies for OpenAI’s developer platform, focusing on adoption, strategic launch planning, and growth of APIs among developers and businesses. You collaborate closely with Product, Engineering, and GTM teams to develop product positioning, messaging, marketing plans, post-launch campaigns, and customer/market insights.
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