Machine Learning Engineer, Perception R&D

About this role:

Pickle is on the hunt for a dynamic and driven Machine Learning Engineer to revolutionize the future of warehouse automation. This role sits at the critical intersection of Perception and MLOps. You will focus on two key pillars: developing robust scene understanding models and building the deployment and analysis infrastructure that allows us to measure, understand, and improve our robots performance in the real world.

Responsibilities:

• Build Performance Analysis Systems: You will design systems that automatically analyze robot telemetry and video logs to detect anomalies, quantify perception failures, and provide deep insights into why a robot performed a certain way.
• Scale the Data Engine: You will take ownership of the data lifecycle. You will build automated pipelines to mine interesting events from fleet data, implement active learning strategies to curate high-value datasets, and drive the continuous improvement of our model suite.
• Develop Scene Understanding Models: You will train and refine 2D/3D vision models specifically designed for event detection and semantic understanding.
• Master Edge Deployment: You will own the optimization and deployment of these models. You will use tools like TensorRT and ONNX to ensure our models run efficiently on edge hardware (NVIDIA Orin).
• Define Perception Metrics: You will move beyond simple validation loss and define real-world performance metrics that correlate with customer success, visualizing these trends for the broader engineering team.

Skills:

• 3 Years of Experience in Computer Vision and Machine Learning, with a strong focus on MLOps, deployment, or data infrastructure.
• Strong proficiency in Python and PyTorch. You are comfortable writing tooling scripts, data queries, and deployment pipelines.
• You have experience optimizing models for the edge using TensorRT, ONNX Runtime, or CUDA. You understand how to profile model performance and manage resource constraints.
• You treat data as a product. You have experience utilizing SQL, data lakes, or vector databases to manage large-scale datasets and automate quality assurance.
• Familiarity with VLMs and 2D/3D Vision architectures sufficient to debug model failures and understand data needs.
• You can translate high-level product requirements into specific engineering tasks and explain technical trade-offs to non-expert stakeholders.
• Familiarity with Docker, AWS/GCP (S3, EC2), labeling platforms and experiment tracking tools.

Pay at Pickle

At Pickle Robot Company, we believe transparency builds trust. The salary range listed here is provided in accordance with Massachusetts law and reflects what we reasonably and in good faith expect to offer for this role. We often consider candidates at different levels of seniority, and final compensation will reflect the level at which a candidate is hired, along with factors like experience and location.

About Pickle Robot

Want to get in on the ground floor of a fast-growing, VC-backed robotics company? Join Pickle Robot We build systems that companies and their teams love.

Pickle robots unload trucks. Every day, millions of trucks and containers are loaded and unloaded, often requiring manual labor—tough, dirty, dangerous, and hard to staff. Pickle automates this process using AI, machine learning, and robotics to deliver reliable products. Our Unload Systems work with teams on loading docks to make the job safer, faster, and more efficient.

Pickle provides best-in-class benefits including health, dental, & vision insurance; unlimited vacation, along with all federal and state holidays; 401K contributions of 5% your salary, travel supplies, and other items to make your working life more fun, comfortable, and productive.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.