Machine Learning Engineer

About the company

Are you tired of missing important moments in your weekly game because no one recorded you? Say goodbye to FOMO and hello to PUSHIT!

PUSHIT allows you to easily catch up on all the highlights you did while playing your sports game.

Our advanced replay system allows you to capture moments previously taken, so you never have to miss anything.

See those videos for more information about PUSHIT:

1. https://www.youtube.com/watch?v=uQrXgheKjCg
2. https://www.youtube.com/watch?v=pmdihAft2f8

See our current mobile apps:

1. IOS
2. Android

What is the job

You’ll be the Machine Learning Engineer on a compact, fast-moving team, working directly with the founders. Your mission: design, train, and ship computer-vision models that recognize sports events and generate highlight videos—running both in the cloud and on edge devices.

Responsibilities

• Build & improve CV models – object detection, action recognition, semantic segmentation, tracking, and event classification for multi-camera sports footage.
• Optimize for the edge – convert and quantize models (TFLite/ONNX/RT-Core) so they run in real time on Raspberry Pi 5 and ARM-based phones.
• Own the ML pipeline – data collection, labeling guidelines, experiment tracking (Weights & Biases / MLflow), automated training, and CI/CD to Kubernetes.
• Collaborate cross-functionally – work with backend, video-encoding, and mobile teams to integrate inference results into our NestJS APIs and Flutter/Angular clients.
• Raise the bar – research state-of-the-art techniques, run ablation studies, author technical specs, and mentor future student interns.

Requirements

• Strong Python (3 yrs+) and deep-learning expertise with TensorFlow and/or PyTorch.
• Solid background in computer vision: convolutional nets, attention mechanisms, spatio-temporal models (e.g., I3D, SlowFast).
• Experience deploying models to mobile or edge hardware (TFLite, Core ML, TensorRT, or similar).
• Familiarity with Docker & Kubernetes workflows for scalable training/inference.
• Proven ability to turn research into production code: at least one project or product in the wild (GitHub, app store, or academic publication).
• Comfortable with version control, code reviews, agile tickets, and writing clear documentation.
• Passion for sports—or at least enough curiosity to spot a backhand winner or a slam-dunk replay!

• English—professional level (our dev team is global).

   

Bonus points

• Experience with video encoding pipelines (FFmpeg, HLS, RTMP) or real-time streaming protocols.
• Knowledge of AWS/GCP services for training at scale (SageMaker, Bedrock, Vertex AI).
• Skills in data annotation tooling or active-learning strategies to shrink labeling budgets.
• Background in satellite or aerial image segmentation, sign-language or face-recognition projects (great analogies to sports CV challenges).

• Love for open-source: contributions to CV libraries.

   

Why PUSHIT?

• Ship features that thousands of weekend athletes use every day.
• Work on cutting-edge CV problems with real-time performance constraints.
• Small team ⇒ huge ownership, zero bureaucracy.
• Fully remote
• Competitive salary, early equity, and budget for conferences & GPUs.