Job Overview
Location
Remote
Job Type
Full-time
Category
Machine Learning Engineer
Date Posted
February 22, 2026
Full Job Description
đź“‹ Description
- • Are you a visionary engineer passionate about shaping the future of autonomous systems? AIM is embarking on an ambitious mission to terraform our planet using cutting-edge autonomy, and we're seeking exceptional talent to join our Machine Learning Platform team. As an MLOps Engineer, you will be instrumental in forging the critical link between groundbreaking Machine Learning research and robust, real-world software deployment. Your role will be pivotal in ensuring a seamless, efficient, and healthy lifecycle for our ML models, from initial development through to physical deployment in our autonomous machines.
- • You will be responsible for the end-to-end design, development, and maintenance of automated CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines specifically tailored for Machine Learning models. This involves creating sophisticated workflows that not only automate the build and deployment processes but also incorporate rigorous validation steps to ensure model safety and reliability before any physical deployment.
- • A key aspect of your role will be optimizing the performance of our machine learning models. This includes building and refining inference engines to achieve low latency and high throughput, ensuring our autonomous systems can react instantaneously and effectively in dynamic environments.
- • You will tackle the complex challenge of managing GPU resource allocation on embedded devices. This requires a delicate balance between achieving optimal inference performance (latency) and adhering to strict power constraints, a critical factor for our mobile robotic platforms.
- • Provisioning and managing scalable cloud infrastructure will be a core responsibility. You will design and maintain environments that can support large-scale distributed training clusters, enabling us to train complex models efficiently, as well as robust simulation environments for testing and validation.
- • We rely on massive streams of multi-modal data, including LiDAR, Camera, and IMU data. You will design and maintain the data pipelines capable of ingesting, synchronizing, and processing these diverse data sources at scale. This includes building the sophisticated storage and retrieval layers that are the foundation of our training datasets.
- • Implementing comprehensive monitoring solutions is crucial. You will build systems to track model performance in production, detect data drift, and ensure the overall health of our ML systems. This includes developing telemetry to monitor system health and inference latency directly on the edge, providing real-time insights vital for robot safety.
- • To ensure the highest level of reproducibility and traceability, you will implement and manage Feature Stores and strict Data Versioning mechanisms. This ensures that every decision made by our robots can be traced back to the exact code, data, and model version used, a critical requirement for safety-critical autonomous systems.
- • You will be the custodian of AIM's ML data platform, continuously maintaining and evolving its capabilities. This includes managing large-scale multi-modal data pipelines, defining and enforcing schema and metadata standards, optimizing dataset indexing, and enhancing high-throughput storage and retrieval layers to support our demanding training, evaluation, and simulation workflows.
- • Collaborate closely with ML researchers and software engineers to understand their needs and translate them into robust, scalable MLOps solutions. You will be a key enabler for our ML teams, providing them with the tools and infrastructure to iterate rapidly and deploy with confidence.
- • Contribute to the architectural design and technical roadmap of our ML platform, ensuring it remains at the forefront of MLOps best practices and can scale to meet the company's ambitious growth objectives.
- • Troubleshoot and resolve complex issues across the ML lifecycle, from data ingestion to model deployment and monitoring.
- • Stay abreast of the latest advancements in MLOps, cloud computing, and machine learning technologies, and proactively identify opportunities to integrate them into our platform.
🎯 Requirements
- • 3+ years of professional experience in MLOps, DevOps, or Software Engineering, with a strong focus on productionizing ML models.
- • Proficiency in Python and C++.
- • Proven experience optimizing Deep Learning models using frameworks like CUDA, ONNX, and TensorRT.
- • Experience building large-scale ML data lakes and dataset pipelines (e.g., Ray Datasets, Ray Data) for ingestion, transformation, versioning, and retrieval of multi-modal training data.
- • Strong command of containerization and orchestration technologies such as Docker and Kubernetes.
- • Hands-on experience with cloud architecture (e.g., AWS, GCP, Azure).
- • Mastery of CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions).
- • Familiarity with handling non-structured robotics data formats (e.g., Rosbags, MCAP, PCD files).
🏖️ Benefits
- • Company-funded medical, dental, and vision insurance.
- • 401k retirement plan.
- • Life insurance.
- • Gourmet food and other office perks.
- • Opportunity for rapid growth and significant impact on company direction.
- • Opportunity to travel to unique global sites.
Skills & Technologies
Python
Docker
Kubernetes
Kafka
Apache Spark
Remote
Degree Required
About Aim Technologies, Inc.
Aim is a technology company focused on providing AI-powered solutions for the energy industry. Their platform leverages artificial intelligence and machine learning to optimize operations, improve safety, and enhance decision-making for oil and gas companies. Aim's core offering includes predictive maintenance, production optimization, and risk management tools. By analyzing vast amounts of data, they help clients reduce costs, increase efficiency, and minimize environmental impact. The company operates within the rapidly evolving energy tech sector, aiming to drive digital transformation and sustainability in a traditionally complex industry.
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