Pear Vc
Member of Technical Staff, Machine Learning - NomadicML
Austin, TX, California, United States · On-site · FullTime
Posted over 6 months ago: check it’s still open
Apply on the employer’s site ↗See how well you match this job
You apply directly with the employer. ROLIVA does not submit applications from this page and is not the employer.
About the role
Description from the employer’s posting. Check the employer’s page for the current version.
- EmployerPear Vc
- LocationAustin, TX, California, United States
- Work styleOn-site
- Employment typeFullTime
- Employer posted
About NomadicML
Americans drive over 5 trillion miles a year, more than 500 billion of them recorded. Buried in that footage is the next frontier of machine intelligence. At NomadicML, we’re building the platform that unlocks it.
Our Vision-Language Models (VLMs) act as the new “hydraulic mining” for video, transforming raw footage into structured intelligence that powers real-world autonomy and robotics. We partner with industry leaders across self-driving, robotics, and industrial automation to mine insights from petabytes of data that were once unusable.
NomadicML was founded by Mustafa Bal and Varun Krishnan, who met at Harvard University while studying Computer Science.
Mustafa is a core contributor to ONNX Runtime and DeepSpeed with deep expertise in distributed systems and large-scale model training infrastructure
Varun is an INFORMS Wagner Prize Finalist for his research in large-scale driver navigation AI models and one of the top chess players in the US.
Show full description
Our team has built mission-critical AI systems at Snowflake, Lyft, Microsoft, Amazon, and IBM Research, holds top-tier publications in VLMS and AI at conferences like CVPR, and moves with the speed and clarity of a startup obsessed with impact.
About the Role
We’re seeking a Machine Learning Engineer who thrives at the frontier of foundation-model research and production engineering.
You’ll help define how machines learn from motion: training and fine-tuning large-scale Vision-Language Models to reason about complex, real-world video.
Your work will involve building multi-modal architectures that perceive, localize, and describe motion events (turns, lane changes, interactions, anomalies) across millions of frames, and turning those breakthroughs into robust APIs and SDKs used by enterprise customers.
You’ll work directly with the founders to:
Train and evaluate VLMs specialized for motion understanding in autonomous-driving and robotics datasets.
Design and scale GPU-accelerated pipelines for training, fine-tuning, and inference on multi-modal data (video + language + sensor metadata).
Build agentic evaluation frameworks that benchmark spatiotemporal reasoning, localization accuracy, and narrative consistency.
Develop and productionize curation loops that use our own models to generate and refine datasets (“AI training AI”).
Publish high-impact research (e.g., NeurIPS, CVPR) while shipping features that customers use immediately.
You’ll Excel If You Have
Strong proficiency in Python, PyTorch, and large-scale ML workflows.
Research experience in foundation models, VLMs, or multi-modal learning (publications/patents a plus).
Ability to iterate quickly and autonomously, running experiments end-to-end.
Experience training or fine-tuning models on video or sensor data.
Understanding of retrieval systems, embeddings, and GPU optimization.
Nice to Have
Contributions to open-source ML frameworks (e.g., DeepSpeed, Hugging Face).
Experience with vector databases, distributed training, or ML orchestration systems (e.g., Ray, Kubeflow, MLflow).
Prior exposure to autonomous-driving or robotics datasets.
Check your resume against this job Free, no sign-up. Nothing you paste is stored.
Staying safe while you apply
- Never pay to apply, interview or accept an offer. ROLIVA never asks you for payment to apply to a job.
- Apply only through the official “Apply on the employer’s site” button, on the employer’s own site, and check that a recruiter’s email uses that employer’s own domain rather than a free email address.
- Be cautious of an offer made with no real interview, or pressure to decide quickly or share financial details early.
Does something look wrong with this listing? Use “Report broken link” in the role details, or read ROLIVA’s full job-safety guidance ↗.
Preparing for this role with ROLIVA
- Check the fit.
Compare the requirements with the experience you have confirmed, including gaps.
- Prepare honestly.
Build interview stories and application drafts from your own evidence.
- Keep track.
Save the role and record your own application status and follow-ups.
At a glance
- Work styleOn-site
- Employment typeFullTime
- SeniorityLead / Principal
- Employer posted
- Added to ROLIVA
- Last checked
Skills in this posting
Skills ROLIVA found in the employer’s own description above. A mention is not a requirement.