Founding Controls​/Neural Networks Engineer

About Hayla

Hayla is building the intelligent direct current (DC) power layer for next-generation AI infrastructure. As data centers transition to higher-efficiency, high-voltage DC architectures — including emerging 800-volt systems — new hardware and control layers are required to deliver power with precision, safety, and real-time responsiveness. Hayla is developing that platform.

We are a pre‑seed company backed by leading investors and operators. Joining now means helping define the architecture, engineering culture, and technical foundations of a new category in AI power systems.

We are seeking a controls and neural networks engineer to develop the real‑time intelligence that manages power flows across Hayla’s distributed DC platform. You will build multi‑timescale control systems, integrate telemetry from embedded nodes, and design algorithms that enable stable, efficient, and fault‑tolerant operation.

This role spans modeling, simulation, applied ML, algorithm development, and close collaboration with embedded and hardware teams.

• Develop real‑time control algorithms that manage power routing and system behavior
• Build decision systems operating on multiple timescales (millisecond, second, minute)
• Integrate telemetry from distributed embedded controllers into centralized inference models
• Design neural‑network-, optimization-, or rule‑based controllers to improve balancing and fault response
• Simulate system dynamics to validate performance and predict behavior under varying load conditions
• Work with embedded engineers to map algorithmic outputs into hardware‑safe actions
• Collaborate with power electronics teams to understand physical constraints and protection requirements
• Build tools for evaluation, visualization, and monitoring of control performance
• Participate in architecture reviews, documentation, and iterative refinement of control algorithms

• 3+ years of experience in controls engineering, applied machine learning, or real‑time systems
• Strong background in control theory, optimization, dynamical systems, or reinforcement learning
• Experience building algorithms that operate under real‑time or near‑real‑time constraints
• Proficiency with Python and simulation frameworks (numerical modeling, system simulation, or RL environments)
• Ability to reason about physical systems, stability, sensing limits, and fault conditions
• Comfortable working with embedded and hardware teams to deploy algorithms in real systems
• Thrives in a fast‑moving, experimental environment

• Experience with power systems, microgrids, robotics, or distributed physical systems
• Background in graph neural networks, RL‑based control, or model‑predictive control
• Familiarity with telemetry pipelines, data streaming, or real‑time monitoring
• Exposure to embedded control, sensing hardware, or actuator integration
• Prior startup or advanced R&D experience

Design the intelligence behind the power layer for future AI data centers.

Shape a real‑time control system operating on hardware that’s never existed before.

Collaborate across power, embedded, thermal, and system architecture.

Earn meaningful founder‑level equity as part of an early technical team.