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Machine Learning Engineer

HAYSTACK MOBILE TECHNOLOGIES, LLC
United States
3 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source
arc.dev

Tech stack

A/B Testing Artificial Intelligence Computer Vision Continuous Integration Python (Programming Language) Machine Learning Tensorflow Pytorch Generative AI Low Latency Data Analytics Machine Learning Operations

Job description

  • Lead the end-to-end development of AI solutions using Computer Vision, Machine Learning, Generative AI, and data science.
  • Generate actionable insights for player performance, contextual statistics, and injury risk.
  • Integrate model-driven insights into personalisation engines, tailoring recommendations.
  • Define advanced experimental designs, lead A/B testing, and establish robust MLOps practices.
  • Design, architect, and operate low latency, highly reliable cloud-based AI systems for live sports scenarios.
  • Influence roadmaps, architecture, and platform evolution for production ML systems.

Requirements

  • Extensive lead-level engineering experience delivering data-driven ML systems.
  • Working knowledge of modern ML techniques, including Generative AI.
  • Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch, TensorFlow).
  • End-to-end MLOps experience, including CI/CD for ML, experiment tracking, and model registries.
  • Proven technical leadership experience, including mentoring Data Scientists.
  • Adaptability and ability to support teams in fast-changing environments.

Benefits & conditions

  • Opportunity to rethink how sports are experienced with an AI-driven platform.
  • Professional growth in a dynamic and innovative tech environment.
  • Hybrid working approach with 2 days a week onsite.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

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Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

Explore playlists

2:35 min

Preventing remote code execution in PyTorch models

Balázs Kiss · World Congress 2023

1:39 min

Fundamentals of tensors and the TensorFlow library

Håkan Silfvernagel · LIVE

54 sec

Generating multiple hook options for outreach A/B testing

Leandro Gomes da Silva Leandro Gomes da Silva · World Congress 2025

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

3:53 min

Architecting machine learning projects with the PAI platform

Qiyang Duan · LIVE

56 sec

Performing local A/B testing across multiple AI agents

Julia Kasper · Coffee With Developers

Videos

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Machine learning in the browser with TensorFlowjs thumbnail
38:24
LIVE • over 5 years ago

Machine learning in the browser with TensorFlowjs

HÃ¥kan Silfvernagel

DevOps for Machine Learning thumbnail
44:40
World Congress 2021 • about 5 years ago

DevOps for Machine Learning

Hauke Brammer

Bringing the power of AI to your application. thumbnail
32:26
World Congress 2024 • about 2 years ago

Bringing the power of AI to your application.

Krzysztof Cieślak

Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding) thumbnail
29:32
World Congress 2026 Europe • about 2 months ago

Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)

Christoph Lohrmann, Utz Bacher

How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale) thumbnail
27:23
World Congress 2023 • almost 3 years ago

How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)

Dora Petrella

Getting Started with Machine Learning thumbnail
44:11
LIVE • almost 5 years ago

Getting Started with Machine Learning

Alexandra Waldherr

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