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.

Principal AI Systems Engineer — Agentic Platforms

The Moment

The role of the software engineer is changing. Autonomous agents can now execute real workflows, operate infrastructure, and improve over time. The hard problems are shifting from model demos to production systems: orchestration, memory, reliability, control, and security.

OpenAI acquired OpenClaw. Meta paid $2B for Manus. The agent platform layer is becoming one of the most important layers in the stack.

At Kindo, we’re already there. Our platform runs autonomous agents in production at real enterprises, automating DevOps and SecOps workflows with real permissions, real consequences, and real reliability requirements.

About Kindo

Kindo is an agent automation platform for DevOps and SecOps teams. We help organizations automate high-friction operational work using autonomous agents that run reliably, securely, and at scale. Our platform supports deployment on-prem, in hybrid environments, or in the cloud, with enterprise-grade security controls from day one.

We’re a small, highly technical team with strong customer traction and real enterprise revenue. Engineers have direct ownership over critical systems and shape the platform’s technical direction and long-term evolution.

The Role

You will define and evolve the architectural foundations of Kindo’s agent platform. This is applied systems engineering at the frontier of AI-native development, not ML research and not chatbot wrappers. You’ll work on agent execution frameworks, memory architectures, multi-model execution, secure tool-calling integrations, and the platform primitives that determine what autonomous systems can reliably do.

This role requires invention and unusually strong technical judgment. Many of the paradigms for agentic systems are still emerging. You’ll continuously track the ecosystem, explore new approaches, prototype quickly, and decide what becomes the platform’s durable foundation. You will identify the highest-leverage architectural opportunities, the failure modes most likely to bite us, and the guardrails and abstractions that let the system scale safely.

Principal engineers at Kindo are builders and inventors who help determine what the future of agentic systems should look like, while ensuring the platform remains reliable, secure, observable, debuggable, and maintainable under real-world conditions.

What You’ll Build

• Agent execution architectures, including autonomous task loops, scheduling, triggers, control planes, and failure isolation
• Retrieval and memory architectures, including persistent, structured, and contextual memory systems
• Multi-model routing and orchestration across providers, balancing quality, latency, cost, and failure modes
• Tool-calling and integration frameworks for safe interaction with external services and enterprise environments
• Reliability, security, and operability foundations, including evaluation, observability, auditing, and recovery paths
• Core platform primitives and architectural patterns that enable entirely new autonomous capabilities safely

How You Build

AI is a first-class tool in how we engineer. You use AI across design, prototyping, implementation, testing, debugging, and incident response, and you continuously refine workflows that increase leverage without sacrificing quality. At this level, you also help define the engineering paradigm itself: how we use autonomous agents and AI-driven workflows to compound velocity over time while minimizing slop, security risk, and architectural drift.

You build with discipline. You create boundaries, verification strategies, and operational guardrails so systems remain understandable and controllable as autonomy increases. You ensure the platform can evolve rapidly without losing reliability and security.

What We’re Looking For

We care far more about what you’ve built than what’s on your resume.

You:

• Have deep expertise designing and operating complex backend or distributed systems in production
• Have built and evolved platform-level architectures that remained durable under rapid change
• Have built LLM-powered or AI-native systems beyond demos, with real users, constraints, and failure modes
• Demonstrate exceptional architectural judgment around reliability, security, observability, and long-term system evolution
• Have invented or introduced foundational abstractions, workflows, or architectural approaches that materially improved system capability or engineering effectiveness
• Actively track emerging tools, models, and approaches and translate the best of them into production systems
• Use AI as a core part of your engineering workflow, not as an occasional convenience
• Operate with exceptional ownership and take systems end-to-end, including long-term evolution

Technical requirements:

• TypeScript required, Python strongly preferred
• Strong SQL proficiency
• Experience with production infrastructure; Docker/Kubernetes experience is a plus
• Familiarity with enterprise security patterns is a plus
• Domain familiarity with DevOps, SecOps, or infrastructure automation is a plus

Culture

Small team, high autonomy, high ownership. We move fast, prototype aggressively, and ship what works. We maintain high standards around reliability, security, and clarity. We value builders, explorers, and inventors who want to help define the future of agentic systems.

Compensation and Location

Compensation: $250,000–$330,000 base salary plus competitive equity
Location: Venice, San Francisco, Remote, or Hybrid

How to Apply

Send us:

• Your work. A portfolio, demo, GitHub profile, blog post, recorded walkthrough, or any evidence of AI systems you’ve built. We want to see how you think, not just what you’ve done.
• A brief note on what excites you about agentic systems and where you think the hard problems are.
• Your resume, but know that the first two items matter more.

We’ll move fast. The interview process is designed to see how you actually work, not quiz you on trivia.

Staff AI Systems Engineer — Agentic Platforms

The Moment

The role of the software engineer is changing. Autonomous agents can now execute real workflows, operate infrastructure, and improve over time. The hard problems are shifting from model demos to production systems: orchestration, memory, reliability, control, and security.

OpenAI acquired OpenClaw. Meta paid $2B for Manus. The agent platform layer is becoming one of the most important layers in the stack.

At Kindo, we’re already there. Our platform runs autonomous agents in production at real enterprises, automating DevOps and SecOps workflows with real permissions, real consequences, and real reliability requirements.

About Kindo

Kindo is an agent automation platform for DevOps and SecOps teams. We help organizations automate high-friction operational work using autonomous agents that run reliably, securely, and at scale. Our platform supports deployment on-prem, in hybrid environments, or in the cloud, with enterprise-grade security controls from day one.

We’re a small, highly technical team with strong customer traction and real enterprise revenue. Engineers have direct ownership over critical systems and shape how the platform evolves.

The Role

You will define, build, and evolve foundational systems that enable autonomous agents to operate reliably in production. This is applied systems engineering with AI at the center, not ML research and not chatbot wrappers. You’ll work on agent execution frameworks, retrieval and memory systems, multi-model execution, and secure tool-calling integrations that interact with real enterprise environments.

This role also requires invention. Many of the patterns for agentic systems are still emerging. You’ll explore new approaches, prototype quickly, and turn what works into durable platform foundations. You’ll identify high-leverage architectural improvements, abstractions, and guardrails that expand what the platform can do while keeping it reliable, secure, observable, and maintainable under real-world conditions.

Staff engineers at Kindo are builders and inventors with strong architectural judgment. You help define both what we build next and which approaches become the system’s durable defaults.

What You’ll Build

• Agent execution systems, including autonomous task loops, scheduling, triggers, and control planes
• Retrieval and memory architectures, including context management, long-term memory, and structured memory
• Multi-model routing and orchestration across providers, balancing quality, latency, cost, and failure modes
• Tool-calling and integration frameworks for safe interaction with external services and enterprise environments
• Reliability, security, and operability foundations, including evaluation, observability, failure isolation, and recovery paths
• Core platform abstractions and architectural patterns that make new agent capabilities easier to build safely

How You Build

AI is a first-class tool in how we engineer. You use AI across design, prototyping, implementation, testing, debugging, and incident response, and you continuously refine workflows that increase leverage without sacrificing quality. You develop pragmatic guardrails, verification strategies, and architectural boundaries to minimize slop, reduce risk, and keep systems safe as autonomy increases.

You also push the team’s defaults forward through better patterns, reusable workflows, and clearer architectural primitives, so the whole organization builds faster with fewer failure modes.

What We’re Looking For

We care far more about what you’ve built than what’s on your resume.

You:

• Have deep experience building and operating complex backend or distributed systems in production
• Have built LLM-powered or AI-native systems beyond demos, with real users, real constraints, and real failure modes
• Have strong architectural judgment around reliability, security, observability, and system evolution
• Have invented or introduced abstractions, workflows, or architectural approaches that materially improved system capability or engineering effectiveness
• Are comfortable operating in ambiguous frontier areas and validating ideas through rapid iteration
• Use AI as a core part of your development workflow, not as an occasional convenience
• Operate with high ownership and take systems end-to-end, including long-term evolution

Technical requirements:

• TypeScript required, Python strongly preferred
• Strong SQL proficiency
• Experience with production infrastructure; Docker/Kubernetes experience is a plus
• Familiarity with enterprise security patterns is a plus
• Domain familiarity with DevOps, SecOps, or infrastructure automation is a plus

Culture

Small team, high autonomy, high ownership. We move fast, prototype aggressively, and ship what works. We maintain high standards around reliability, security, and clarity. We value builders, explorers, and inventors who want to help define the future of agentic systems.

Compensation and Location

Compensation: $210,000–$260,000 base salary plus competitive equity
Location: Venice, San Francisco, Remote, or Hybrid

How to Apply

Send us:

• Your work. A portfolio, demo, GitHub profile, blog post, recorded walkthrough, or any evidence of AI systems you’ve built. We want to see how you think, not just what you’ve done.
• A brief note on what excites you about agentic systems and where you think the hard problems are.
• Your resume, but know that the first two items matter more.

We’ll move fast. The interview process is designed to see how you actually work, not quiz you on trivia.

Senior AI Systems Engineer — Agentic Platforms

The Moment

The role of the software engineer is changing. Autonomous agents can now execute real workflows, operate infrastructure, and improve over time. The hard problems are shifting from model demos to production systems: orchestration, memory, reliability, control, and security.

OpenAI acquired OpenClaw. Meta paid $2B for Manus. The agent platform layer is becoming one of the most important layers in the stack.

At Kindo, we’re already there. Our platform runs autonomous agents in production at real enterprises, automating DevOps and SecOps workflows with real permissions, real consequences, and real reliability requirements.

About Kindo

Kindo is an agent automation platform for DevOps and SecOps teams. We help organizations automate high-friction operational work using autonomous agents that run reliably, securely, and at scale. Our platform supports deployment on-prem, in hybrid environments, or in the cloud, with enterprise-grade security controls from day one.

We’re a small, highly technical team with strong customer traction and real enterprise revenue. Engineers have direct ownership over critical systems and shape how the platform evolves.

The Role

You will design, build, and operate core systems that enable autonomous agents to function reliably in production. This is applied systems engineering with AI at the center, not ML research and not chatbot wrappers. You’ll build production-grade agentic workflows, retrieval and memory systems, multi-model execution, and tool-calling integrations that interact safely with enterprise systems.

This is also frontier work. Many of the patterns for agentic systems are still emerging. You’ll explore new approaches, prototype quickly, and turn what works into durable production systems. At the same time, strong distributed systems fundamentals still apply. These systems must be reliable, secure, observable, debuggable, and maintainable under real-world conditions.

What You’ll Build

• Agent execution systems, including autonomous task loops, scheduling, triggers, and control planes
• Retrieval and memory architectures, including context management, long-term memory, and structured memory
• Multi-model routing and orchestration across providers, balancing quality, latency, cost, and failure modes
• Tool-calling and integration frameworks for safe interaction with external services and enterprise environments
• Reliability, security, and operability foundations, including evaluation, observability, failure isolation, and recovery paths

How You Build

AI is a first-class tool in how we engineer. You use AI across design, prototyping, implementation, testing, debugging, and incident response, and you continuously refine workflows that increase leverage without sacrificing quality. You pair that velocity with discipline: guardrails, verification, and architectural boundaries that keep systems safe as autonomy increases.

What We’re Looking For

We care far more about what you’ve built than what’s on your resume.

You:

• Have built and operated complex backend or distributed systems in production
• Have built LLM-powered or AI-native systems beyond demos, with real users and real constraints
• Have strong judgment around reliability, security, observability, and failure modes
• Are comfortable operating in ambiguous frontier areas and validating ideas through rapid iteration
• Use AI as a core part of your development workflow, not as an occasional convenience
• Operate with high ownership and autonomy and take systems end-to-end

Technical requirements:

• TypeScript required, Python strongly preferred
• Strong SQL proficiency
• Experience with production infrastructure; Docker/Kubernetes experience is a plus
• Familiarity with enterprise security patterns is a plus
• Domain familiarity with DevOps, SecOps, or infrastructure automation is a plus

Culture

Small team, high autonomy, high ownership. We move fast, prototype aggressively, and ship what works. We maintain high standards around reliability, security, and clarity. We value builders, explorers, and inventors who want to help define the future of agentic systems.

Compensation and Location

Compensation: $170,000–$220,000 base salary plus competitive equity
Location: Venice, San Francisco, Remote, or Hybrid

How to Apply

Send us:

• Your work. A portfolio, demo, GitHub profile, blog post, recorded walkthrough, or any evidence of AI systems you’ve built. We want to see how you think, not just what you’ve done.
• A brief note on what excites you about agentic systems and where you think the hard problems are.
• Your resume, but know that the first two items matter more.

We’ll move fast. The interview process is designed to see how you actually work, not quiz you on trivia.