Job Title: Senior Applied AI/ML Scientist (Deep Learning and Generative Models)

Company Overview: 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.

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.

At the heart of our platform is DeepHat, 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.

Job Description:

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.

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.

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.

Responsibilities:

• 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.
• 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.
• 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.
• 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.
• 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.
• Collaboration & Mentorship: Work closely with cross-functional teams to translate product requirements into technical AI solutions. While this is an individual contributor role, you will be expected to mentor junior members, review code, and contribute to a culture where technical ideas are debated openly and constructively.

Required Qualifications:

• Education: PhD or MS in Computer Science, Machine Learning, AI, or a related field, or equivalent practical experience.
• Experience: 3-5+ years of experience in AI/ML with a strong focus on deep learning, generative models, and LLMs.
• Effective AI Collaboration: You don't just "use" AI tools (e.g. Cursor, Claude Code, Devin, etc.); you know how to effectively partner with them to drive real efficiency. You know how to thoughtfully prompt, delegate, and iterate with these AI to achieve concrete productivity gains and genuinely accelerate your development process.
• Evaluation Expertise: Demonstrated experience designing metrics and building evaluation harnesses for non-deterministic models. You understand that robust evals are the cornerstone of successful AI products.
• Fine-Tuning Expertise: Deep understanding of the post-training landscape, with practical experience in techniques such as SFT, PEFT (LoRA/QLoRA), and Knowledge Distillation.
• Applied Frameworks: Proficiency with the modern open-source LLM ecosystem. Experience with distributed training (e.g., Accelerate, FSDP, DeepSpeed) and high-level modeling libraries.
• Strong Fundamentals: Deep conceptual understanding of the mathematical and architectural underpinnings of AI/ML (gradients, attention mechanisms, loss landscapes). While we don't code in raw TensorFlow/PyTorch daily, we value the first-principles understanding that comes with knowing these layers.
• Systems Engineering: Experience with software engineering best practices and an ability to write clean, production-ready code. You should be comfortable with the "Ops" side of MLOps.
• Problem Solving: Strong critical thinking skills with a pragmatic focus on shipping solutions that work for users, not just maximizing academic benchmarks.
• Location: Hybrid Onsite in Venice, CA (2 days a week).

Preferred Qualifications:

• LLM Production Experience: Practical experience deploying and maintaining LLMs in a production environment.
• Advanced Alignment: Familiarity with emerging alignment research (e.g., DPO, RLVR) and an ability to determine when these complex methods add value over simpler baselines.
• Agentic Concepts: Strong conceptual understanding of Agentic architectures (planning, tool use, ReAct loops, etc.). 
• Enterprise Context: Knowledge of enterprise security best practices and experience with Enterprise SaaS software.
• Research Impact: A track record of staying current with arXiv papers and successfully translating a research concept into a working feature.

What We Offer:

• Competitive salary (Range: $170k-$220k Base) and Equity
• Comprehensive health, dental, and vision insurance
• Unlimited vacation and paid time off policies
• A chance to be part of a groundbreaking company shaping the future of generative AI

Company Culture:

• Start-up and entrepreneurial mindset: High-energy, passionate, and willing to put in a bit of extra energy to ensure we succeed, but mindful of work-life balance, protecting weekends, and accommodating personal obligations. Full belief in the multi-billion dollar market opportunity in front of us.
• Strong ownership culture: Complete ownership of core areas of responsibility, but also welcoming ideas and contributions from everyone. Everyone acts like product owners and contributes where they can provide value.
• Fast-paced and action-oriented: Focus on high velocity, minimal processes, and utilizing existing solutions to maximize product development and minimize maintenance overhead. Focused on outcomes over hours. We work smarter, not harder.
• Empathy, open-mindedness, and constructive feedback: Respect diverse working styles, encourage data-informed decisions, and build understanding and trust through direct conflict resolution
• Reflective thinking and effectiveness: Balance thoughtful planning and discipline with action, prioritize finding solutions that meet customer needs over being right
• Collaboration and support: Foster an environment where employees can work together, rely on each other, and support one another through challenges

If you are a passionate and talented AI researcher looking to make a meaningful impact in the generative AI space applied into end-user products, we would love to hear from you. Apply now and join our team of visionaries in creating the next unicorn company in the AI industry.

About Kindo

Kindo is an agent automation platform purpose-built for DevOps and SecOps teams. We help organizations automate tedious, high-friction operational work—securely—using autonomous agents that can run in the background on schedules or triggers. We’re on-prem capable and built with enterprise security controls from day one.

We’re ~40 people, have strong customer traction, and are entering the next phase of growth where product clarity, focus, and execution velocity matter more than ever.

The Role

As a Forward Deployed Engineer at Kindo, you’ll use your deep technical expertise to collaborate with our most strategic customers to drive transformational AI adoption. You will collaborate closely with Kindo engineers to ship advanced, enterprise ready AI agents that solve real world business problems. 

You’ll work side-by-side with Product and Engineering to get deep into customer workflows and ship real solutions fast. Drawing on strong engineering instincts, hands-on experience with frontier AI, and comfort working directly with customers, you’ll turn messy, high-stakes business problems into reliable, production-ready systems—without compromising on safety or quality.

This is a foundational role as you would be FDE #1, helping to shape our forward-deployed future. This role is ideal for someone who’s excited to build, iterate, and grow alongside our customers.

Responsibilities

• Embed closely with customer teams, understand their needs, and guide adoption of what you build.
• Build long term relationships with customers and proactively identify new opportunities for AI deployment throughout the lifecycle of an engagement.
• Conduct regular health checks, usage reviews, and roadmap alignment sessions.
• Anticipate potential issues and coordinate with support, product, and engineering teams as needed.
• Partner with Sales to support renewals, expansions, and success planning.
• Capture and relay customer feedback to influence product direction.
• Contribute to playbooks, diagnostic tools, documentation, and enablement assets.

What We’re Looking For (Required)

• 3+ years in a technical post-sales or customer-facing role (TAM, Solutions Architect, CSM with technical depth, etc.)
• Strong understanding of cloud infrastructure, SaaS, or DevSecOps environments.
• Production experience with LLMs including advanced prompt engineering, agent development, evaluation frameworks, and deployment at scale.
• Strong programming skills with proficiency in Python (and ideally in one or more additional languages like Typescript, Java, etc) and experience shipping production applications.
• Excellent communication skills - comfortable with both technical and executive audiences.
• Proactive mindset- you spot patterns, solve problems early, and take initiative.
• Collaborative nature- you know how to navigate cross-functional teams and build trust internally.

What We Offer

• Competitive equity package for early employees.
• Comprehensive health, dental, and vision insurance.
• Unlimited vacation and paid time off policies.
• Opportunity to work with world-class leadership in cybersecurity and AI.
• Fast-paced, entrepreneurial startup environment with a strong culture of ownership and innovation.

Company Culture

• High-performance mindset: We set ambitious goals and work with urgency.
• Extreme ownership: Everyone is responsible for their domain and takes pride in delivering results.
• Fast-paced and action-oriented: We prioritize speed and execution over perfection.
• Collaboration and innovation: We challenge each other, share insights, and push boundaries.
• Customer obsession: Everything we do is about making our customers successful.

Compensation

Base salary in the $120K–$150K range + bonus + early employee equity with meaningful ownership in a high-growth AI startup.