AI Research Intern at Archer

San Jose, California, United States -

Full Time

Start Date

Immediate

Expiry Date

24 Mar, 26

Salary

35.0

Posted On

24 Dec, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Deep Learning, Reinforcement Learning, Physics-Based Simulation, Sequence-To-Sequence Modeling, Speech Recognition, Multi-Modal AI, Analytical Skills, Data Processing, Machine Learning, Natural Language Processing, Telemetry Data, Simulation Environments, Communication, Data Collection, Experimentation

Industry

Airlines and Aviation

Description

Archer is an aerospace company based in San Jose, California building an all-electric vertical takeoff and landing aircraft with a mission to advance the benefits of sustainable air mobility. We are designing, manufacturing, and operating an all-electric aircraft that can carry four passengers while producing minimal noise. Our sights are set high and our problems are hard, and we believe that diversity in the workplace is what makes us smarter, drives better insights, and will ultimately lift us all to success. We are dedicated to cultivating an equitable and inclusive environment that embraces our differences, and supports and celebrates all of our team members. This internship is a part of a 10-week full-time summer internship program opportunity. This role will be based at Archer’s headquarters in San Jose, California and relocation or housing will not be provided. Join a world-class AI team dedicated to transforming the future of aviation through cutting-edge research and development. What You’ll Do: Develop and iterate on reinforcement learning algorithms within high-fidelity simulation environments. Engineer reward functions, state-action spaces, and input representations for simulation-based learned policies. Build and refine machine learning models for the processing and interpretation of complex communication streams and telemetry data. Architect solutions for sequence-to-sequence transcription, natural language reasoning, and the extraction of intent from multi-modal data sources. Collaborate with research engineers to configure and optimize simulation setups, ensuring they accurately reflect physical constraints for data generation. Partner with research engineers to prototype solutions from academic literature that address specialized domain vocabularies and high-stakes operational contexts. Prototype and validate promising techniques from academic literature to benchmark model behavior and robustness. Build scripts and tools to support reproducible experimentation and streamlined data collection. Run experiments to benchmark model accuracy and reasoning capabilities across diverse datasets and edge-case scenarios. Communicate progress and research findings to the team in a concise manner while staying current with latest developments. What You Need: Pursuing a degree in Computer Science, Robotics, Engineering, Data Science, or a related technical field. Proficiency in Python and deep learning frameworks (e.g., PyTorch), or modern NLP libraries (Hugging Face, Transformers). Experience with reinforcement learning and physics-based simulation engines. Ability to translate mathematical concepts from research papers into functional code. Familiarity with sequence-to-sequence modeling, speech recognition, or multi-modal AI. Analytical skills with experience handling large-scale or unstructured datasets. Summer 2026 Internship Program Details: This internship is a part of a 10-week full-time summer internship program opportunity. Program dates are: May 26, 2026 - July 31, 2026 June 15, 2026 - August 21, 2026 *We will not be able to accommodate interns outside of these two program dates. This role will be based at Archer in San Jose, CA Relocation and housing will not be provided. Please note that this job description is intended to provide a general overview of the position and does not include an exhaustive list of responsibilities and qualifications. At Archer we aim to attract, retain, and motivate talent that possess the skills and leadership necessary to grow our business. We drive a pay-for-performance culture and reward performance that supports the Company’s business strategy. For this position we are targeting a base pay between $25.00 - $35.00 per hour. Actual compensation offered will be determined by factors such as job-related knowledge, skills, and experience. Archer is committed to working with and providing reasonable accommodations to job applicants with physical or mental disabilities, and those with sincerely held religious beliefs. Applicants who may require reasonable accommodation for any part of the application or hiring process should provide their name and contact information to Archer’s People Team at people@archer.com. Reasonable accommodations will be determined on a case-by-case basis. Information collected and processed as part of any job applications you choose to submit is subject to Archer's Candidate Privacy Policy. Archer is unable to provide work visa sponsorship for this position at the present time. Archer is proud to be an Equal Opportunity employer committed to diversity and inclusivity in the workplace. All aspects of employment are decided on the basis of merit, qualifications, and business needs. We do not discriminate based upon race, color, religion, sex, sexual orientation, age, national origin, disability status, protected veteran status, gender identity or any other characteristic protected by federal, state or local laws. Archer Aviation does not engage with external recruiting agencies/individual recruiters with whom it does not have a prior written agreement. Archer reserves the right to make use of any unsolicited resumes that it receives and bears no responsibility for payment of any fees asserted from the use of unsolicited resumes. If you are a recruiting agency or individual recruiter wishing to do business with Archer, please reach out to People@archer.com. All employment processes are managed by the Archer People Team.

Responsibilities

The intern will develop reinforcement learning algorithms and engineer reward functions within simulation environments. They will also collaborate with research engineers to prototype solutions and run experiments to benchmark model accuracy.