Research Engineer – Machine Learning & Robotics
Company shown to members 🔒 · Remote · US only
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First recorded by Psst: 12 October 2026. Your first 3 jobs are free to apply to (the first right away, 2 more with your email). After that, the company and the apply link are for Psst members, from $4.99 for 7 days.
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The 20-second read
- Experience
- Experience not stated
- Where you can live
- US only
- Skills and tools
- python, aws, machine learning, b2b, aml
- Degree or licence
- Mentioned (bachelor)
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Job details
- Where you can work
- United States
- Employment type
- Not stated by the employer
- Advertised pay
- Not stated by the employer
- Experience
- Experience not stated
- Employer publication date
- 26 June 2026
- First recorded by Psst
- 12 October 2026
- Last checked at the source
- 10 November 2026
About the job
Role Purpose
The company is looking for a Research Engineer with a foundation in machine learning, robotics, and data infrastructure to help build and scale the systems used for data collection, model development, and product improvement.
This role sits at the intersection of robotics, computer vision, and applied machine learning. You will work hands-on with robotic systems, ROS/ROS2-based modules, mobile data collection workflows, and ML pipelines that support training, evaluation, and production model performance. This is a strong opportunity for a new graduate or early-career engineer who wants to build practical systems that directly improve real-world AI products.
Role Value
High-quality data and reliable model evaluation infrastructure are critical to improving the company’s machine learning and computer vision capabilities. This role helps ensure that data collected from robotic systems and mobile applications is usable, scalable, and connected to the broader model development lifecycle.
The Research Engineer will support both the robotics/data collection environment and the ML development workflow, helping the team move faster, improve model quality, and better understand model performance in production.
Example Responsibilities
- Build and integrate ROS/ROS2-based modules to support robotic navigation, manipulation, and data collection workflows.
- Replicate and integrate mobile and web UI environments into robotic testing and data collection systems.
- Build, maintain, and improve training and test datasets collected through robotic manipulators and in-house iOS and Android applications.
- Mine, query, and analyze data from internal databases to create features, identify trends, and generate insights that improve product and model development.
- Develop tools and processes to monitor data quality, model performance, and model accuracy in production environments.
- Implement end-to-end machine learning workflows, including data preparation, model training, testing, evaluation, and deployment support.
- Write clean, modular, well-documented C++ and Python code that can be maintained and extended by other engineers.
- Collaborate cross-functionally with machine learning, engineering, product, and research teams to improve data collection, model development, and system performance.
Required Experience
- Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field.
- 1–2 years of relevant industry, internship, or research experience in machine learning, robotics, computer vision, or related technical areas.
- Hands-on experience with ROS and/or ROS2, including building or integrating modules for robot navigation, manipulation, simulation, or data collection.
- Strong foundation in machine learning fundamentals, with experience implementing models in Python using frameworks such as PyTorch, TensorFlow, scikit-learn, or similar.
- Experience working with databases, writing queries, and building or maintaining data pipelines for training, testing, or evaluation.
- Strong programming skills in Python and C++, with an emphasis on clean, reliable, well-documented code.
- Ability to work hands-on with physical hardware, debug system behavior, and translate research or prototype work into scalable engineering solutions.
Nice to Have
- Experience with robotic manipulators, mobile robot platforms, or lab-based robotic systems.
- Familiarity with iOS and/or Android development, especially for hardware-integrated data collection applications.
- Experience with data collection pipelines for computer vision, biometric systems, identity verification, or similar applied AI domains.
- Exposure to production ML observability, model monitoring, drift detection, or data quality monitoring tools.
- Familiarity with cloud platforms such as AWS, including S3, EC2, SageMaker, or similar tools for storage, compute, and model deployment.
- Experience working in cross-functional environments with machine learning engineers, software engineers, researchers, and product teams.
The company Values
IDEAL: Integrity, Diversity, Empowerment, Accountability, Leading Innovation
Equal Opportunities
The company is a collaboration of people with different ideas, strengths, interests and cultures. We welcome applications and colleagues from all backgrounds and of all statuses.
About the company
The company is a B2B technology company dedicated to eradicating online identity fraud, money laundering and other financial crimes to help make the internet safer. We leverage AI, biometrics, machine learning, liveness detection and automation to create solutions that are trusted by leading brands worldwide and respected by industry thought leaders.
The company is the leading provider of online identity verification, eKYC and AML solutions. With a global footprint, we’re expanding the team to meet strong client demand across a range of industries including Financial Services, Travel, Sharing Economy, Fintech, Gaming, and others.
Applicant Data Privacy
We will only use your personal information in connection with the company’s application, recruitment, and hiring processes, as described in the company’s Applicant Privacy Notice. If you have any questions or comments, please send an email to [link for members] .
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