AI / Machine Learning Infrastructure Engineer opening - Physical AI Startup

AI / Machine Learning Infrastructure Engineer opening - Physical AI Startup

San Francisco, CA (you can work from home 2x/week)

The company’s leadership team includes one of the leading minds in modern computing, an early engineer at Palantir, and a world renowned security expert.

The company's product involves physical AI, spatial intelligence, and multimodal LLMs. It is making real world infrastructure (like airplanes, trains, automobiles, the global supply chain, etc.) more efficient, safe, and stable.

The company has 12 employees and 5 engineers, and is planning on hiring 6-10 people (including 3-5 engineers) in the next year.

The company has raised seed funding and is in process of closing series A in early 2026. This is an opportune time to join since it would be shortly before the series A round closes, which would translate to more favorable equity, including founder shares which are stock grants rather than stock options.

In this position, you would be mostly working with Go and Python. You would report to the CEO and Founder who is also an engineer. You’d be working in a variety of areas including optimizing core vision pipelines, model acceleration, building efficient operators, and resource efficiency.

The company will pay up to $220k in salary, plus equity which could be lucrative.

Job Responsibilities:

- Mostly programming in Go and Python.

- Building the engine that powers the visual sensing platform. Providing the tools to automate the lifecycle of the AI workflow, including model development, evaluation, optimization, deployment, and monitoring across thousands of video streams.

- Optimizing Core Vision Pipelines: Identifying key bottlenecks in the current video analytics pipeline and performing in-depth analysis to ensure the best possible performance on current server and edge compute architectures.

- Cross-Stack Collaboration: Collaborating closely with AI research and platform engineering teams to optimize core parallel algorithms and influence the design of the next-generation inference infrastructure.

- Model Acceleration: Applying advanced model optimization techniques—such as quantization (Int8/FP16), pruning, and layer fusion—to Vision Transformers (ViTs) and CNNs to maximize throughput and minimize latency.

- Building Efficient Operators: Working across the entire ML framework/compiler stack (e.g., PyTorch, CUDA, TensorRT, and NVIDIA DeepStream) to write custom optimized ML operator libraries.

- Resource Efficiency: Reducing the compute cost per video stream to enable massive scalability of the SaaS product.

Qualifications:

- A Bachelors degree and/or higher in Computer Science, or equivalent experience.

- Strong programming skills in Go or C or Rust or C .

- Experience with model optimization, quantization, and efficient machine learning and deep learning techniques (e.g., knowledge distillation, pruning).

- Deep understanding of GPU hardware performance, including execution models, thread hierarchy, memory/cache management, and the cost/performance trade-offs of video processing.

- Experience with profiling and benchmarking tools such as Nsight Systems or Nsight Compute to validate performance on complex architectures.

- Experience identifying and resolving compute and data flow bottlenecks, particularly in high-bandwidth video processing pipelines.

- Strong communication skills and the ability to work cross-functionally between research and infrastructure teams.

Nice to have:

- Experience with Computer Vision, Deep Learning, and Vision Transformers.

- Experience with video processing frameworks such as NVIDIA DeepStream, DALI, or FFmpeg.

- Familiarity with ML compilers (e.g., TVM, MLIR) or inference engines like TensorRT or ONNX Runtime.

- Knowledge of distributed training systems or cloud-scale inference serving (e.g., Triton Inference Server).

About Skyrocket Ventures

Skyrocket Ventures is a recruiting firm for hundreds of high growth technology companies that range from industry leaders to top-tier startups. This opportunity is with one of our client companies for a full-time permanent hire. Please only apply if you are authorized to work in the U.S.

Please note that even if this job is not a perfect match, we encourage you to apply as long as it is in the ballpark. Companies are often flexible in hiring candidates who do not perfectly fit their written job description, as long as the most important qualifications are there and the candidate is good in general.

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