Machine Learning Service Lead (Autonomy)

Job Description: Machine Learning Service Lead (Autonomy)

About KrateoSky

KrateoSky is building the trusted Western champion in AI aerial robotics—a unified perception and actuation platform that will make the skies the world's most trusted and transformative engine of productivity. We are not a drone company. We are the autonomy infrastructure powering robotic flight and aerial intervention across defense, industry, and public safety. We are executing a rapid roll-up strategy to consolidate best-in-class technologies, creating the first at-scale, AI-native alternative.

Speed and scale define our competitive advantage. We move fast by questioning assumptions, removing complexity, and rapidly demonstrating over analyzing. We scale by building certifiable platforms on reusable architectures. Our AI-native, digitally connected ecosystem amplifies this advantage, automating tedious work so engineers focus on novel problem-solving. We keep our back office small and our organization flat to maintain velocity and operate as one integrated team with real-time visibility into trade-offs, risks, and progress.

You're joining at the beginning when your decisions define how we scale to Western leadership. We're building a dream team of high performers to establish the processes, tools, and culture that enable rapid acquisition integration and platform scalability for missions that matter. No PowerPoint purgatory, no "that's how aerospace does it" sacred cows. First principles, data-driven decisions, hands-on execution.

Position Summary

The Machine Learning Service Lead (Autonomy) is a critical leadership and hands-on engineering role within our centralized Autonomy and Software Capability team.

In this role, you will be the technical owner and delivery lead for the common Machine Learning services and models deployed across all KrateoSky aerial platforms, driving the creation of a unified, reusable autonomy asset.

You will lead the design, training, and deployment of edge-native computer vision, perception, and navigation models. Working in close collaboration with our Cloud Architect, you will establish the central MLOps infrastructure, data ingestion pipelines, and a unified data lake to receive and manage video, telemetry, and flight data from across our platforms. Furthermore, you will collaborate closely with the Head of AI Innovation to drive the edge deployment of transformer-based architectures, generative AI, and agentic systems directly on our drones. To succeed, you must be a first-principles systems thinker who is equally comfortable writing robust training code, designing model packaging interfaces, and collaborating with embedded systems engineers to deploy state-of-the-art perception onto flying hardware.

Player-coach role: You will be hands-on from day one. You must be comfortable doing the work yourself before you have a team to delegate to. We are looking for a builder who has shipped real systems—not PowerPoints, not just theoretical plans that never left the whiteboard. You think in systems but can get deep into the details when needed.

You are AI-native in how you work: AI agents, automation and reusable workflows are your default operating model. You generate boilerplate, training loops, dataset management structures, and documentation with LLMs, and you use AI for model optimization, profiling, and debugging. You see AI as the reason a small, elite team can outperform a traditional organization three times its size.

Embody our values and behaviors—deliver precision and quality, challenge how things are done, break problems into fundamentals, automate relentlessly, hold yourself accountable, and deliver together no matter the obstacles. You must exhibit Above the Line behaviors: operating as a Coach, Creator, and Challenger rather than a Victim, Hero, or Villain. You apply automation-first thinking: your default instinct is 'how do we eliminate this manual task' not 'who can I assign this to.' You act as a humble teacher, prioritizing ego-free knowledge sharing and documentation discipline. Finally, you maintain a strong PDCA (Plan-Do-Check-Act) orientation, demonstrating a willingness to experiment, learn from failure, and iterate.

Key Responsibilities

• Core Functional Ownership (ML Service Delivery): Serve as the technical authority for KrateoSky's common Machine Learning Service. Define, develop, and maintain reusable ML perception capabilities (such as object detection, multi-object tracking, classification, depth estimation, visual-inertial odometry, and semantic segmentation) delivered across our platforms.

• AI-Native Execution: Make AI-augmented workflows the default; automate repetitive tasks; use AI for analysis, documentation, and error-catching; target 3-5x productivity gains over traditional approaches. Concretely, you will:

  • Use AI-augmented development environments (e.g., Cursor, LLM assistants) to rapidly generate boilerplate code, training loops, and test suites.

  • Leverage LLMs for automated generation of synthetic data generation prompt definitions, edge models benchmarking scripts, and sensor-fusion integration testing.

  • Automate data pipeline ingestion validation and data lake anomaly detection scripts.

• System Building & Documentation: Build ML processes and model packaging interfaces from the ground up that are mature enough to be taught, documented, and replicated across future KrateoSky acquisitions. Package and optimize deep learning models for resource-constrained, SWaP-C limited embedded environments.

• M&A Integration: Assess AI and ML capabilities of acquisition targets for technical and domain maturity; lead post-acquisition harmonization of models, pipelines, and tools; identify and retain key ML talent.

• Team Leadership & Culture: Start as a hands-on individual contributor; build and lead a high-performing team of ML and MLOps engineers within the first 6–12 months, fostering a culture of ownership, craftsmanship, and aggressive execution.

• Cross-Functional Collaboration: Partner closely with the Chief AI Officer, Head of AI Innovation, Cloud Architect, and decentralized product line Chief Engineers to integrate and validate common ML services, ensuring seamless feedback loops and transitioning transformer-based models to edge-native execution.

Required Qualifications

• Proven Technical Leadership: 8+ years of professional software and machine learning engineering experience, with progressively increasing functional leadership.

• Proven Track Record: You have personally trained, optimized, and shipped complex ML models that operate on physical, safety-critical hardware in the field (aerial robotics, autonomous vehicles, computer vision systems, or defense systems).

• Technical Depth: Expert-level fluency with modern deep learning frameworks (PyTorch, TensorFlow) and edge inference engines (TensorRT, ONNX Runtime, TVM). Deep knowledge of quantization (INT8/FP16 precision trade-offs), pruning, sensor fusion, visual-inertial odometry (VIO), SLAM, and multi-modal perception. Comfort making build-vs-buy decisions for ML infrastructure.

• AI-Native Proficiency: AI assistants (e.g., Cursor, LLM-driven test generation) are your default development environment; you have achieved measurable productivity gains from AI-augmented workflows.

Desired Qualifications

• Aerospace & UAS Flight Experience: Direct experience with drone flight stacks (PX4, ArduPilot, ROS/ROS2) and hardware-in-the-loop (HIL) or software-in-the-loop (SIL) simulation environments.

• Synthetic Data Generation: Hands-on experience using photorealistic simulation tools (e.g., NVIDIA Omniverse, Unity, Unreal Engine) to generate synthetic data for training and verifying edge ML perception models.

• Transformer & Multi-Task Architectures: Familiarity with the deployment of vision transformers (ViTs), multi-task learning networks, or lightweight vision-language models (VLMs) on embedded hardware.

• Prior M&A Involvement: Prior involvement in M&A technical/domain due diligence or post-acquisition integration.

• Security Clearance Eligibility: Eligibility to obtain a U.S. Security Clearance is a plus (for internal evaluation of classified data requirements).

Logistics & Compensation

• Reports To: Autonomy Capability Leader

• Location: Remote (US-based)

• Travel %: N/A

• Compensation: $131,000 - $203,000 (Targeted base; total package includes comprehensive benefits)