Full Time Vlm Jobs (NOW HIRING)

Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. 

As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. 

Vision-Language Models (VLMs) are a foundational pillar of our Autonomy stack. In this Staff
Research Engineer role, you will play a key role in delivering the overarching VLM strategy,
especially training, shipping, optimizing the VLM models, as well as extending to
multi-modalities and enabling new use cases, among others. In this role, you will also be
responsible to define and deliver VLM-driven solutions to solve some of autonomy's hardest
challenges, including automated data mining, handling long-tail distributions, rare edge-case
detection, and scene anomaly reasoning. As part of the model delivery, you will also own the
whole end-to-end lifecycle of VLM model delivery: data acquisition, metrics definition,
benchmarking, model performance optimization, deployment, feedback loop.

Drive and deliver the VLM model strategy: Define, drive and execute the roadmap of
VLM model delivery, including training and delivering VLM models, optimization,
deployment, as well as the extension to other multi-modalities.
Accelerate data mining: Design and deliver VLM/LLM related models and strategies
that power automated data mining, long-tail distributions, rare/edge case detection, and
anomaly detection at scale, across multiple modalities (vision, lidar, text, etc).
Iterate and optimize performance: Establish rigorous evaluation and monitoring
benchmarks. Identify and root-cause top-tier system anomalies, prioritizing high-impact
optimizations to continuously push the needle on performance.
Cross-functional collaboration: Partner closely with core Autonomy teams
(Perception, Planning, Calibration, Systems, etc) to translate vehicle feature
requirements into concrete ML deliverables.
Influence trade-offs & requirements: Define system requirements and guide
cross-functional efforts through technical trade-off decisions.

Education: BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a
highly related quantitative field.
Experience: 5+ years of professional experience scaling ML solutions, with a strong
focus on the following:
VLM model training: Hands-on experience training or fine-tuning VLMs using
modern parameter-efficient techniques (LoRA, QLoRA) and RL alignment.

Large-scale data mining: Proven track record developing VLM/LLM-related
techniques for data mining, long-tail distributions, rare cases, safety-critical
events.
Zero/few-shot capabilities: Experience with open-vocabulary, zero-shot, or
few-shot classification models, particularly in long-tail scenarios.
System engineering: Strong proficiency in Python alongside a solid
understanding of modern Perception pipelines, benchmarking tools, and
infrastructure.
Execution: Demonstrated ability to root-cause complex issues across a
distributed, cross-functional stack in a fast-paced environment.

Preferred Qualifications
Experience applying VLMs within the Autonomous Vehicle domain.
Experience with Auto Prompt Optimization (APO) and automated prompt engineering
techniques.
Experience with spatial grounding in 2D and/or 3D.
Experience extending foundational models to extra modalities (e.g., LiDAR, Radar, IMU,
ego-motion).
Experience utilizing VLMs or Foundation Models for complex behavior reasoning and
planning.
Experience with onboard edge deployment, cloud inference architectures, and balancing
compute/efficiency trade-offs.
Experience with quantization techniques (PTQ, QAT) and high-performance inference
engines like TensorRT.

The listed base salary range for this role is $228,00 - $285,000 for San Francisco Bay Area based applicants. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, geographic location, shift, and organizational needs.

We offer a comprehensive package of benefits for full-time and part-time employees, their spouse or domestic partner, and children up to age 26, including but not limited to paid vacation, paid sick leave, and a competitive portfolio of insurance benefits including life, medical, dental, vision, short-term disability insurance, and long-term disability insurance to eligible employees. You may also have the opportunity to participate in Rivian’s 401(k) Plan and Employee Stock Purchase Program if you meet certain eligibility requirements. Full-time employee coverage is effective on their first day of employment. Part-time employee coverage is effective the first of the month following 90 days of employment. More information about benefits is available at rivianbenefits.com. 

Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information or characteristics, physical or mental disability, marital/domestic partner status, age, military/veteran status, medical condition, or any other characteristic protected by law.

Rivian is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, such as those covered by the Americans with Disabilities Act, that requires accommodations to assist you in the search and application process, please email us at candidateaccommodations@rivian.com.

Rivian may collect, use and disclose your personal information or personal data (within the meaning of the applicable data protection laws) when you apply for employment and/or participate in our recruitment processes (“Candidate Personal Data”). This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security and preference information. Rivian may use your Candidate Personal Data for the purposes of (i) tracking interactions with our recruiting system; (ii) carrying out, analyzing and improving our application and recruitment process, including assessing you and your application and conducting employment, background and reference checks; (iii) establishing an employment relationship or entering into an employment contract with you; (iv) complying with our legal, regulatory and corporate governance obligations; (v) recordkeeping; (vi) ensuring network and information security and preventing fraud; and (vii) as otherwise required or permitted by applicable law. 

Rivian may share your Candidate Personal Data with (i) internal personnel who have a need to know such information in order to perform their duties, including individuals on our People Team, Finance, Legal, and the team(s) with the position(s) for which you are applying; (ii) Rivian affiliates; and (iii) Rivian’s service providers, including providers of background checks, staffing services, and cloud services. 

Rivian may transfer or store internationally your Candidate Personal Data, including to or in the United States, Canada, the United Kingdom, and the European Union and in the cloud, and this data may be subject to the laws and accessible to the courts, law enforcement and national security authorities of such jurisdictions.  

How We Use AI in Our Hiring Process: To ensure transparency, we want candidates to know that Rivian uses iCIMS Talent Cloud Artificial Intelligence (TCAI) and AI-enabled tools to assist with screening, reviewing, organizing and highlighting profiles and applications that match the key requirements for each role.

Participation in AI profile matching is entirely voluntary. If you prefer that your profile not be used in this process, you can opt out at any time. Opting out means your profile will be excluded from automated matching and will not be surfaced for additional roles through this system. Your current application remains active and will not be affected in any way.

Please note that we are currently not accepting applications from third party application services.

Education: BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, or a
highly related quantitative field.
Experience: 5+ years of professional experience scaling ML solutions, with a strong
focus on the following:
VLM model training: Hands-on experience training or fine-tuning VLMs using
modern parameter-efficient techniques (LoRA, QLoRA) and RL alignment.

Large-scale data mining: Proven track record developing VLM/LLM-related
techniques for data mining, long-tail distributions, rare cases, safety-critical
events.
Zero/few-shot capabilities: Experience with open-vocabulary, zero-shot, or
few-shot classification models, particularly in long-tail scenarios.
System engineering: Strong proficiency in Python alongside a solid
understanding of modern Perception pipelines, benchmarking tools, and
infrastructure.
Execution: Demonstrated ability to root-cause complex issues across a
distributed, cross-functional stack in a fast-paced environment.

Preferred Qualifications
Experience applying VLMs within the Autonomous Vehicle domain.
Experience with Auto Prompt Optimization (APO) and automated prompt engineering
techniques.
Experience with spatial grounding in 2D and/or 3D.
Experience extending foundational models to extra modalities (e.g., LiDAR, Radar, IMU,
ego-motion).
Experience utilizing VLMs or Foundation Models for complex behavior reasoning and
planning.
Experience with onboard edge deployment, cloud inference architectures, and balancing
compute/efficiency trade-offs.
Experience with quantization techniques (PTQ, QAT) and high-performance inference
engines like TensorRT.