Research Engineer, Training & Inference

About the role

ABOUT HARMONIC

At Harmonic, we believe the future of AI depends on proof. Most AI makes its best guess. Aristotle uses formal methods and reinforcement learning to check its own reasoning and results.

AI agents can write software in seconds, but edge cases and silent bugs still get through. We're building the verification engine to find them before code ships. With support for C and Verilog, Aristotle is built for the hardware and software where failure is expensive.

Backed by some of the world’s most prominent investors, we are intentionally scaling an elite technical team.

Try Aristotle for yourself here https://aristotle.harmonic.fun/!

ABOUT THE ROLE

We are developing reinforcement learning systems at a scale where standard abstractions frequently fail. Unlike labs that operate primarily through high-level wrappers, we own the entirety of our RL stack. This ownership spans from low-level environment simulators and custom communication primitives to our distributed training loops and inference engines.

We are seeking engineers who view existing libraries as a baseline and the hardware speed itself as the true target. You will be responsible for the architecture powering our agents, with a relentless focus on maximizing the throughput of our reinforcement learning and production workflows.

KEY RESPONSIBILITIES

- Total Stack Ownership: Maintain and optimize our proprietary RL training and serving infrastructure. You have the authority to refactor any layer—from the Python API down to the CUDA kernels—to achieve peak performance for foundation model workloads.

- Optimized Training: maximize the throughput of our reinforcement learning system from data generation to model training with sharded multi-node training and inference algorithms.

- High-Performance Serving: optimize our inference stack for high-throughput reinforcement learning and low-latency LLM production traffic. Tune the inference engine, router, and scheduler, down to custom kernels if need be.

- Compute Optimization: Identify and resolve performance bottlenecks within our distributed clusters, ensuring optimal throughput and memory efficiency for multi-billion parameter models, balancing memory constraints with compute-heavy training cycles.

MINIMUM QUALIFICATIONS

- BS in Computer Science or a related technical field, or equivalent industry experience

- 2+ years of relevant, hands-on industry experience

- Proficiency in Python

- Experience building or maintaining components within ML frameworks (e.g., PyTorch, JAX, or TensorFlow).

- Proficiency in either: - Understanding of distributed training concepts and collective communication primitives (e.g., NCCL). OR - Practical experience deploying and profiling models on GPU-accelerated cloud infrastructure.

PREFERRED QUALIFICATIONS

- MS or PhD in Computer Science, Mathematics, or a related field.