Machine Learning Engineer

Posted 5 days agoViewed

💎 Seniority level: Middle, 4+ years

📍 Location: England, Scotland, Portugal, Poland, Spain

🔍 Industry: Robotics

🏢 Company: Locus Robotics👥 251-500💰 $117,000,000 Series F over 2 years agoWarehousing Logistics Industrial Automation E-Commerce Warehouse Automation Robotics

🗣️ Languages: English

⏳ Experience: 4+ years

🪄 Skills: AWSDockerPythonSQLCloud ComputingData AnalysisKubernetesMachine LearningPyTorchAlgorithmsData sciencePandasTensorflow

Requirements:

4+ years of hands-on experience designing and deploying machine learning models in production, with a focus on reinforcement learning (RL) and multi-agent systems (MAS).
Advanced Python programming skills, with a strong emphasis on writing efficient, scalable, and maintainable code.
Proven experience with TensorFlow/PyTorch/Jax, Scikit-learn, and MLOps workflows.
Experience working with Polars and/or Pandas for high-performance data processing.
Proficiency with cloud platforms (AWS, GCP, or Azure), including containerization and orchestration using Docker and Kubernetes.
Hands-on experience with reinforcement learning frameworks such as OpenAI Gym or Stable-Baselines3.
Practical knowledge of optimization algorithms and probabilistic modeling techniques.
Experience integrating models into real-time decision-making systems or multi-agent RL environments (MARL).

Responsibilities:

Utilize, develop, and enhance simulation tooling and infrastructure.
Develop, deploy, and maintain machine learning models, with a strong focus on reinforcement learning (RL) and multi-agent systems (MAS).
Implement and improve MLOps pipelines.
Collaborate with data engineers and software developers to ensure seamless integration of machine learning models with existing infrastructure and data pipelines.
Stay up to date with advancements in reinforcement learning, distributed computing, and ML frameworks to drive innovation in the organization.
Work with cloud-based solutions (AWS, GCP, or Azure) to deploy and manage machine learning workloads in a scalable manner.

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