AI/ML Training Performance Engineer

Blackrock Neurotech; Salt Lake City (UT); On-site; USD 120,000 - 190,000; Full time

Job summary

Blackrock Neurotech, based in Salt Lake City, UT, seeks an ML Training Performance Engineer to optimize large-scale model training across GPUs and clusters. You’ll work hands-on across Python, C++, and GPU kernels while collaborating with researchers and IT teams to scale compute efficiently.

You will own performance baselines, profiling, and tooling, ensuring numerical correctness and scientific intent as models grow in complexity and data. Occasional on-site work is required.

Qualifications

• Demonstrated experience improving the performance of substantial deep learning training workloads with measurable gains

Responsibilities

• Own training performance across single-GPU, multi-GPU, and multi-node workloads with reproducible baselines
• Profile the full training path to identify bottlenecks and impact
• Optimize tensor layouts, memory, and precision to fit larger models
• Write and validate custom GPU kernels using CUDA, Triton, or HIP
• Improve Python scripts, training configs, batching, and optimizers
• Design distributed training strategies based on model structure and memory limits
• Collaborate with researchers on hardware-aware changes and convergence impact
• Coordinate with infra teams on GPU selection, cloud configs, and containers

Skills

Deep learning optimization

GPU kernel development

Profiling & benchmarking

Python & C++ programming

PyTorch knowledge

Distributed training

Linux GPU environments

Big data / HPC concepts

Education

Bachelor’s degree in CS/Engineering or equivalent

Tools

CUDA

HIP/ROCm

Triton

TensorRT

Job description

Build the systems that expand human capability

At Blackrock Neurotech, we’ve spent decades making the impossible possible – helping people move, speak, and reconnect with the world when they otherwise could not. We’ve seen that restoring function restores more than ability. It restores independence, identity, and agency.

Today, we are building the next generation of human capability: brain-computer interfaces that are designed to be safe, scalable, and trusted in the real world. Our work is not only about reconnecting people to what was lost, but about expanding what is possible – creating a seamless interface between human intent and technology.

This is foundational work in a category-defining field. You will help build the infrastructure for a future where neural interfaces are invisible, reliable, and deeply human-centered.

Working At Blackrock Neurotech Means

• Owning meaningful, high-impact problems at the frontier of science and engineering
• Building alongside experienced, thoughtful peers across disciplines
• Solving technically complex challenges grounded in real human outcomes
• Contributing to a culture that values rigor, clarity, and long-term thinking over noise

The Role

The ML Training Performance Engineer will own the efficiency and scalability of training models on GPU and cloud infrastructure. You will turn available compute into faster, more capable experiments as our model training scales in complexity and compute requirements.

As a hands-on individual contributor on a small research team, you will work across the training stack, from Python and model execution to GPU kernels, distributed communication, and runtime environments. Partnering with model researchers, data engineers, and infrastructure and IT teams, you will identify and implement performance improvements while preserving numerical correctness and scientific intent.

You will have significant ownership over how we measure, optimize, and scale training performance, establishing the baselines, tooling, and technical approaches that will support our AI/ML work as it grows.

What You’ll Do

• Own training performance across single-GPU, multi-GPU, and multi-node workloads, establishing reproducible baselines for throughput, memory use, utilization, time to target quality, and cost
• Profile the full training path to distinguish compute, memory, communication, CPU, and I/O bottlenecks and prioritize changes with measurable end-to-end impact
• Optimize tensor layouts, precision, memory allocation, activation checkpointing, operator fusion, and execution graphs to fit larger or longer-context models within available resources
• Write, tune, and validate custom GPU kernels using CUDA, Triton, HIP/ROCm, or the appropriate platform tools when existing implementations limit performance
• Improve Python training scripts, framework and compiler settings, batching, gradient accumulation, and optimizer execution while preserving intended training behavior
• Design and tune distributed training strategies, including data, tensor, pipeline, or sharded parallelism, based on model structure, memory limits, and interconnect topology
• Partner with model researchers on hardware-aware architecture and hyperparameter changes, measuring their effects on convergence, model quality, and compute requirements
• Coordinate with the neural data infrastructure engineer on prefetching, pinned memory, host-to-device transfer, and I/O overlap so data delivery keeps pace with training
• Work with infrastructure and IT on GPU selection, cloud instance configurations, networking, drivers, containers, scheduling, and capacity planning as training needs and compute capacity scale
• Build robust checkpoint, restart, and recovery workflows and performance regression checks that keep long-running experiments reproducible and productive
• Communicate benchmark evidence, numerical tradeoffs, scaling limits, and resource recommendations clearly to researchers and organizational stakeholders

What You Bring

• Demonstrated experience improving the performance of substantial deep learning training workloads, with measured gains in speed, memory efficiency, or compute cost
• A measurement-driven approach to performance optimization, using profiling and benchmarking to validate meaningful end-to-end improvements
• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience
• Exceptional programming ability in Python and C++ or a comparable systems language, with strong debugging, testing, and performance analysis practices
• Deep understanding of GPU execution, including memory hierarchies, memory coalescing, thread blocks, warps or wavefronts, occupancy, synchronization, and bandwidth limits
• Hands-on experience developing and profiling GPU kernels with CUDA or HIP/ROCm, and the ability to diagnose correctness and performance at the hardware level
• Deep knowledge of PyTorch or an equivalent framework, including automatic differentiation, computation graphs, tensor storage, compilation, and mixed-precision training
• Strong understanding of deep learning architectures and the underlying computations that drive training performance
• Experience with distributed training, collective communication, sharding, and the interaction between model partitioning and GPU interconnects
• Strong understanding of numerical stability and the ability to validate gradients, convergence, and model quality after performance changes
• Experience configuring and diagnosing Linux-based GPU environments, containers, cloud compute, and high-throughput storage or networking
• Ability to collaborate closely with researchers and infrastructure teams and make clear tradeoffs between implementation effort, performance, reliability, and scientific value
• Experience with Triton, compiler optimization, advanced GPU profiling tools, multiple accelerator generations, long-sequence or multimodal models, neural time series, or large-scale model training is a plus

Working Location

This is an on-site role based at Blackrock Neurotech's headquarters in Salt Lake City, Utah. Occasional travel may be required.

How We Work

We are a small, experienced team working on consequential problems.

• We take ownership of outcomes and follow through with clarity and accountability
• We prioritize sustained, high-quality work over performative urgency
• We value rigor, sound judgement and thoughtful decision-making
• We collaborate deliberately: low ego, high trust and high context

This is a high-ownership role, but it is not an "always-on" one. We expect strong work and our people to have a life outside of it.