Role Description We’re looking for a highly skilled AI Engineer to help us design, build, and scale intelligent systems across our next-generation global HR Tech platform. As an early-stage startup, we’re looking for someone who thrives in dynamic environments, is passionate about automation and AI-driven innovation, and brings hands-on experience developing AI/ML solutions within enterprise-grade SaaS platforms. This role reports directly to the Head of Engineering. - Design and implement machine learning models to solve HR-specific challenges (e.g., intelligent automation, anomaly detection, personalized experiences) - Collaborate with product managers and engineers to bring AI features to life across the platform - Develop and optimize data pipelines to support model training and inference - Research and apply NLP, LLMs, and other AI/ML technologies to streamline and improve HR workflows - Monitor model performance and retrain/improve based on feedback and usage - Build scalable AI solutions using cloud-native tools (preferably AWS) - Contribute to data governance, ethical AI use, and compliance Qualifications - 4–6+ years of experience as an AI/ML Engineer or Data Scientist, ideally with startup or SaaS exposure - Strong understanding of supervised/unsupervised learning, NLP, neural networks, and generative AI - Proficiency with Python and ML frameworks like TensorFlow, PyTorch, Hugging Face, or similar - Experience deploying models in production using cloud platforms like AWS Sagemaker, GCP Vertex AI, or Azure ML - Experience with data versioning, MLOps, and model lifecycle management - Ability to work autonomously and thrive in a startup environment - Familiarity with HR tech, payroll, or workforce data is a plus

Role Description Kumo is building the next generation of AI for structured data. With our Relational Foundation Model (RFM), we help some of the world’s largest companies transform their data into predictions, decisions, and end-to-end automated systems. Our culture is collaborative, fast-moving, and deeply user-obsessed. We value people who take initiative, learn quickly, communicate clearly, and enjoy solving hard technical + people challenges. Demand for Predictive AI is accelerating faster than ever. Our customers include some of the world’s most influential enterprises across retail, e-commerce, consumer goods, fintech, travel, and technology. - Operate at true global scale - Hundreds of ML models - Billions of data points - Business-critical use cases across recommendations, forecasting, supply chain optimization, fraud, CRM, and more We are rapidly expanding our Applied Machine Learning team, a high-impact, highly technical group that sits at the center of our customer engagements. This team guides customers from their very first pilot all the way through to scaled, production-grade deployments of relational predictive models. This is a unique opportunity for someone who is: - Curious and intellectually hungry - Energized by startup culture - Motivated by high-growth environments - Excited to become an expert practitioner of cutting-edge AI models - Thrilled by the chance to work directly with Silicon Valley innovators, global brands, and leaders in data science and business What You’ll Do - Support and eventually own technical success for enterprise customers adopting the Kumo platform. - Design and build prototypes, workflows, and models across use cases such as: - Recommendations & personalization - Forecasting & demand planning - Fraud detection & risk modeling - Supply chain & logistics optimization - Banking & financial analytics - CRM/growth marketing & user modeling - Work hands-on with large-scale relational datasets, customer pipelines, and production ML systems. - Guide customers through modeling choices, data structures, evals, trust, interpretability, and rollout plans. - Translate ambiguous customer needs into concrete ML solutions and RFM workflows. - Collaborate closely with Kumo engineering and research teams to improve platform capabilities. - Act as a technical leader and trusted advisor, understanding that deploying ML is as much a people and business challenge as it is a technical one. - Deliver demos, workshops, best practices, and partner with executives, PMs, analysts, and data scientists. Qualifications - Bachelor’s or Master’s in a STEM field (CS, EE, Math, Physics, Stats, etc.). - Strong fundamentals in data science, statistics, or machine learning coursework. - Real-world experience via internships, research, industry work, or substantial project work. - Demonstrated intellectual curiosity and initiative, personal ML/AI projects, open source, research, hackathons, or other hands-on experience. - Strong communication skills; comfortable working with people and navigating technical + non-technical audiences. - Genuine enthusiasm for ML/AI, modern modeling approaches, and applying them to real business problems. - Motivated, self-driven, excited to learn fast, and comfortable in a high-velocity startup environment. Requirements - Deeper expertise in one or more of: - ML infrastructure / data engineering - Full-stack development for ML apps - LLM orchestration, agent systems, or model tuning - Large-scale distributed systems - Forecasting, recsys, fraud, or other applied ML domains - Familiarity with GNNs, temporal models, or structured reasoning. - Enterprise integrations, data platforms, or productionizing ML Success Looks Like (First 3–6 Months) - Support and eventually lead multiple major customer engagements, delivering real business impact. - Solve multiple challenging predictive machine learning problems, by applying data science skills to large-scale datasets. - Build prototypes and workflows using RFM that demonstrate value and drive adoption. - Collaborate with engineering to improve reliability, performance, and model quality across use cases. - Earn trust from customer technical teams and become their go-to person for ML strategy and execution. Benefits - Exposure to an extraordinary range of challenges and industries. - Learn faster due to diverse customer problems and datasets. - Support and eventually lead technical engagements with large, forward-thinking companies. - Build advanced predictive systems using GNNs, temporal models, forecasting engines, and next-generation workflows. - Work cross-functionally with engineering, ML research, product, and executive leaders. - Help define what enterprise ML looks like in practice.

• As a Senior AI Engineer focused on Agentic Evaluation and Verification and Validation (V&V), you will join the AI and Data Science team within Slingshot’s Research and Development organization. • You will contribute to advancing how intelligent systems are evaluated and validated for mission-critical space operations. • This role focuses on building and scaling evaluation frameworks, benchmarks, and simulation-backed validation systems for agentic AI systems, including multi-step, tool-using, and autonomous decision-making workflows powered by LLMs and reinforcement learning. • Your work will directly support the development of reliable and trustworthy autonomous mission planning systems. • You will partner closely with AI researchers and domain experts to translate real-world mission concepts into structured, testable evaluation systems.

Role Description We’re looking for AI and Machine Learning Engineers to join our Expert Network to help train and evaluate the next generation of LLMs using deep technical expertise. If you have the necessary experience, we’ll send you a quick 10- to 15-minute test to assess your skills and suitability for AI tasks. If successful, you’ll be invited to join Prolific as a participant, where you’ll get paid to train and evaluate powerful AI models. Researchers looking for your skills tend to pay up to $80 per hour. You must be prepared to complete paid tasks that require one hour of uninterrupted work, though many are shorter. Qualifications - Education: a BS, MS, or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field with a focus on Machine Learning. - Professional Experience: experience building, deploying, or fine-tuning ML models in a production environment. - Deep Learning Mastery: professional-level understanding of neural network architectures (Transformers, CNNs, RNNs) and optimization techniques. - LLM Specialization: hands-on experience with Prompt Engineering, RLHF (Reinforcement Learning from Human Feedback), or RAG (Retrieval-Augmented Generation) workflows. - Technical Rigor: the ability to audit complex model logic, identify training data contamination, and evaluate mathematical proofs behind ML algorithms. - Analytical Critique: high attention to detail in spotting "hallucinations," biased outputs, or logical failures in AI-generated technical content. Requirements - Evaluate LLM Architecture Logic: review AI-generated explanations of model architectures, loss functions, and backpropagation for technical accuracy. - Audit Code & Notebooks: validate ML-specific code (e.g., training loops, data preprocessing scripts, or model evaluations) for efficiency and correctness. - Refine RLHF Frameworks: provide the high-quality human feedback necessary to align models with human intent, safety, and helpfulness. - Analyze Model Reasoning: critically assess how an AI model navigates complex chain-of-thought (CoT) prompts and identify where the reasoning breaks down. - Benchmark Performance: conduct comparative testing between different model outputs based on specific technical taxonomies and performance metrics. Key Technologies - Frameworks: expert proficiency in PyTorch or TensorFlow/Keras. - Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers. - Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain. - Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation. Benefits - Competitive pay rates. - Flexible hours. - Ability to work from home.