Machine Learning Engineer...

Locus Robotics is a global leader in warehouse
automation, delivering unmatched flexibility and unlimited
throughput, and actionable intelligence to optimize operations.
Powered by LocusONE, an AI-driven platform, our advanced autonomous
mobile robots seamlessly integrate into existing warehouse
environments to enhance efficiency, reduce costs, and scale
operations with ease. Trusted by over 150 industry leading retail,
healthcare, 3PL, and industrial brands in over 350 sites worldwide,
Locus enables warehouse operators to achieve rapid ROI, minimize
labor costs, and continuously improve productivity. Our
industry-first Robots-as-a-Service (RaaS) model ensures ongoing
innovation, scalability, and cost-effectiveness without the burden
of significant capital investments. With proven capabilities in
diverse workflows—from picking and replenishment to sorting and
pack-out—Locus Robotics empowers businesses to meet peak demands
and adapt to ever-changing operational needs. Are you a Machine
Learning Engineer with a passion for reinforcement learning,
multi-agent systems, and simulation at scale? We want to hear from
you! At Locus Robotics, we’re developing advanced simulation tools
and ML systems to optimize the behavior of large autonomous fleets
in dynamic environments. In this role, you will work on
cutting-edge reinforcement learning (RL) models, multi-agent
systems, and faster-than-real-time simulations to drive innovation
in logistics, robotics, and beyond. You’ll collaborate with a
highly skilled team of engineers and data scientists to develop
scalable ML models and deploy them into production environments
using modern MLOps practices. If you're excited about solving
real-world optimization problems, building high-performance ML
infrastructure, and working with autonomous agent simulations, this
is your opportunity to make a significant impact. This is a remote
position based in England, Scotland, Portugal, Poland, or Spain.
Candidates must be authorized to work in one of these countries
without the need for work sponsorship. Responsibilities: - Utilize,
develop, and enhance simulation tooling and infrastructure to
enable faster-than-real-time modelling of 1,000+ autonomous agents
for various use cases such as fleet optimization, logistics, or
robotics. - Develop, deploy, and maintain machine learning models,
with a strong focus on reinforcement learning (RL) and multi-agent
systems to optimize fleet behavior in dynamic environments. -
Implement and improve MLOps pipelines to support continuous
training, deployment, monitoring, and scaling of machine learning
models in production. - 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. Qualifications: - Master’s degree or Ph.D. in Data
Science, Computer Science, Mathematics, or a related field. - 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 for
training, deployment, and monitoring of ML models. - 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 (e.g., Bayesian methods, Gaussian Belief Propagation). -
Experience integrating models into real-time decision-making
systems or multi-agent RL environments (MARL). - Exposure to
spatiotemporal data analysis, including time-series anomaly
detection and forecasting. - Familiarity with ROS (Robot Operating
System) for robotics or simulation integration. - Publications in
top-tier conferences/journals (e.g., NeurIPS, ICML, ICRA, CVPR,
ECCV, ICCV) are a plus. - Proficient English written and verbal
communication skills required to collaborate effectively with
internal and external teams. - Excellent analytical and
problem-solving skills, with the ability to contribute effectively
in a collaborative team environment. #J-18808-Ljbffr