Machine Learning Engineer (Video Foundation Models & Agentic AI)

Maxinsights

Santa Clara (CA)

On-site

USD 180,000 - 240,000

Full time

14 days+

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Benefits offered by this job

Health insurance

Vision care

Dental coverage

401(k)

Paid holidays

PTO (Paid Time Off)

Sick leave

Job summary

Maxinsights is seeking a talented ML engineer to design and optimize video-language foundation models for real-world applications. You will build end-to-end ML pipelines covering data processing, training, evaluation, and inference and tackle failures to push performance.

We value hands-on experience with PyTorch, multimodal models, and large-scale training, plus the ability to own projects from concept to production while collaborating with product and engineering teams.

Qualifications

Bachelor's/Master's/PhD in Computer Science, AI, ML or related field, or equivalent practical experience.
Strong understanding of deep learning and hands-on experience with model training, fine-tuning, or inference.
Proficiency in Python and deep learning frameworks such as PyTorch.
Familiarity with modern LLMs, multimodal models, or computer vision models.
Strong problem-solving skills and the ability to independently implement, debug, and optimize ML systems.
Ability to work in a fast-paced environment, take ownership, and deliver practical solutions.

Responsibilities

Design, train, fine-tune, and optimize video-language foundation models for real-world applications.
Build and improve large-scale ML pipelines covering data processing, training, evaluation, and inference.
Analyze model failures, identify performance bottlenecks, and drive continuous improvements through data and algorithmic iteration.
Develop efficient model inference and deployment solutions balancing quality, latency, and cost.
Collaborate with product and engineering teams to translate business requirements into technical solutions for production-ready ML capabilities.
Explore and evaluate emerging techniques in multimodal learning, reinforcement learning, and agentic AI for practical problems.
Take ownership of ML projects from problem definition and experimentation through deployment and ongoing improvement.

Skills

Python

Deep learning

Model training

Fine-tuning

Inference

PyTorch

LLMs

Multimodal models

Education

Bachelor's/Master's/PhD in Computer Science / AI / ML or equivalent

Tools

PyTorch

Job description

Job Description

About The Team

We are building Max VLM, a next-generation video foundation model designed to understand human activities, object interactions, and the physical world through first-person video.

Our mission is to develop intelligent video understanding systems that can accurately interpret complex real-world activities and turn continuous visual observations into structured, actionable information.

As a fast-moving startup team, we work across the entire machine learning lifecycle, from data collection and model training to evaluation, optimization, and production deployment. We combine state-of-the-art multimodal AI technologies with real-world data and practical engineering to build scalable, reliable, and efficient AI systems.

Project Overview & Technical Challenges

Despite rapid advances in multimodal large language models, understanding real-world video remains a challenging problem. Existing systems often struggle with fine-grained activity recognition, temporal reasoning, long-video comprehension, and computational efficiency.

Our team focuses on building high-performance video foundation models and bringing them into real-world applications. Key technical directions include:

1 - Video Foundation Models & Multimodal Understanding

Fine-grained action recognition, temporal grounding, and video understanding.
Long-video comprehension, temporal reasoning, and event-level understanding.
Multimodal model fine-tuning, post-training, and performance optimization.
High-quality training data generation, automated labeling, and data selection.
Model evaluation, error analysis, and data-driven iteration to continuously improve production performance.

2 - Efficient Model Training & Deployment

Large-scale model training, fine-tuning, and distributed training optimization.
Efficient video processing, and inference acceleration. Model compression, quantization, and optimization of latency, throughput, and computational cost.
Production model serving, monitoring, and continuous performance improvement.
End-to-end optimization of model quality, reliability, and deployment efficiency.

3 - Agentic Video Understanding & Advanced Post-Training

Develop agentic video understanding systems with autonomous tool use and multi-step reasoning.
Explore reinforcement learning and advanced post-training techniques to improve model performance.
Apply emerging AI research to real-world applications and product improvements.

Key Responsibilities

Design, train, fine-tune, and optimize video-language foundation models for real-world applications.
Build and improve large-scale ML pipelines covering data processing, training, evaluation, and inference.
Analyze model failures, identify performance bottlenecks, and drive continuous improvements through data and algorithmic iteration.
Develop efficient model inference and deployment solutions that balance quality, latency, and computational cost.
Collaborate closely with product and engineering teams to translate business requirements into technical solutions and deliver production-ready ML capabilities.
Explore and evaluate emerging techniques in multimodal learning, reinforcement learning, and agentic AI, applying promising advances to practical problems.
Take ownership of ML projects from problem definition and experimentation through deployment and ongoing improvement.

Qualifications

Minimum Qualifications

Bachelor's, Master's, or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field, or equivalent practical experience.
Strong understanding of deep learning and hands-on experience with model training, fine-tuning, or inference.
Proficiency in Python and deep learning frameworks such as PyTorch.
Familiarity with modern LLMs, multimodal models, or computer vision models.
Strong problem-solving skills and the ability to independently implement, debug, and optimize machine learning systems.
Ability to work in a fast-paced environment, take ownership, and deliver practical solutions to complex problems.

Preferred Qualifications

Strong research capabilities, demonstrated through publications at top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV) or other impactful research contributions.
Experience with video understanding, vision-language models, or multimodal foundation models.
Experience with LLM/VLM post-training, including SFT, reinforcement learning, or reasoning-oriented training.
Experience with distributed training, efficient inference, or production-scale ML systems.
Familiarity with agentic AI, tool-augmented reasoning, or multimodal reasoning.
Proven ability to translate novel research ideas into practical implementations and measurable improvements.

Why Join Us?

Real-World Impact: Build video AI systems that directly power real-world products and reach users through production deployment.
End-to-End Ownership: Work across the complete ML lifecycle, from data and model development to optimization and deployment, with the opportunity to own meaningful technical initiatives.
Challenging MIL Problems: Solve practical and technically challenging problems in video understanding, large-scale model training, and efficient inference.
Frontier AI Exploration: Stay close to advances in multimodal foundation models, reasoning, reinforcement learning, and agentic AI, with opportunities to apply new ideas to real-world problems.
Startup Environment: Work in a small, fast-moving team that values ownership, technical excellence, rapid iteration, and measurable impact.

Default Benefits