Senior Applied AI/ML Scientist

<p><strong>Job Title: Senior Applied AI/ML Scientist (Deep Learning and Generative Models)</strong></p> <p><strong>Company Overview:</strong> Kindo is an agent automation platform for DevOps and SecOps teams. We help organizations automate high-friction operational work using autonomous agents that run in the background — reliably, securely, and at scale.</p> <p>We're ~40 people with strong customer traction, real enterprise revenue, and the infrastructure to support serious AI development. This isn't a side bet on AI. It's the entire company.</p> <p>At the heart of our platform is <strong>DeepHat</strong>, Kindo's uncensored cybersecurity model. Built for real offensive reasoning, long-context analysis, and secure execution, DeepHat serves as the "AI Brain" of our platform. It is trained on real-world attack patterns and high-signal security data to power precise, autonomous workflows. As a member of the DeepHat team, you will be responsible for pushing the boundaries of what specialized LLMs can achieve in the realm of digital defense.</p> <p><strong>Job Description:</strong></p> <p>We are seeking a highly experienced Senior Applied AI/ML Scientist with a specialization in deep learning and generative models to join our dynamic team. In this role, you will play a pivotal part in the architecture and implementation of our AI modeling efforts, with a specific focus on post-training and fine-tuning large language models.</p> <p>While this role requires deep technical expertise, we value a collaborative environment where good ideas flourish regardless of title. You will work within a team that empowers engineers to own their stack end-to-end. The ideal candidate has strong AI/ML fundamentals and can bridge the gap between theoretical research and practical production systems. You should be comfortable working with Agentic LLM usage and modern fine-tuning approaches, ranging from Supervised Fine-Tuning to Knowledge Distillation to RL, to create robust, reliable enterprise solutions.</p> <p>You will be an early engineer at a high-growth startup, playing a significant role in building and bringing a new generative AI product to market. We are looking for a systems-thinker who cares as much about model evaluation and production stability as they do about algorithmic innovation.</p> <p><strong>Responsibilities:</strong></p> <ul> <li>Rigorous Evaluation (Evals): Architect, build, and maintain comprehensive evaluation pipelines. You believe that "you can't improve what you don't measure," and you will be responsible for creating evals that accurately reflect production performance and agentic reliability.</li> <li>Post-Training Fine-Tuning: Apply expert knowledge of model alignment and post-training strategies to tailor our in-house LLMs for enterprise capabilities. You should be adept at selecting and implementing the right technique for the job. Whether that is Supervised Fine-Tuning (SFT), Knowledge Distillation, PEFT, or leveraging alignment methods like Reinforcement Learning from Verifiable Rewards (RLVR) when appropriate.</li> <li>Hands-on Data Engineering Strategy: Go beyond just "using" data. You will be responsible for the end-to-end data lifecycle—sourcing, cleaning, curating, and modifying datasets to maximize model effectiveness and domain specificity.</li> <li>Training Inference Optimization: Utilize and optimize the open-source LLM ecosystem for the full model lifecycle. You will leverage distributed training tools (e.g., Accelerate, DeepSpeed/FSDP) and high-throughput serving engines (e.g., vLLM, TensorRT-LLM) to ensure efficiency from training to production.</li> <li>End-to-End Ownership: Take a systems-level approach to AI. You will not just build models in isolation but will be responsible for the E2E lifecycle of the model, including how it integrates into production and interacts with the broader application.</li> <li>Collaboration Mentorship: Work closely with cross-functional teams to translate product requiremen ... (truncated, view full listing at source)