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Onnx Engineer Jobs

2 jobs found

5d ago

Machine Learning Engineer

Escalon Santa Monica, CA

Santa Monica, CA

5d ago

$100k - $120k/yr Full time Artificial Intelligence Convolutional Neural Networks Deep Learning

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Job Description

Escalon is seeking a Machine Learning Engineer in Santa Monica, CA to design and implement intelligent systems that extract predictive and semantic value from computer vision and behavioral datasets. The role centers on building models that learn shared representations from visual inputs, enabling tasks such as future action prediction, semantic matching, and similarity-based inference.

This is an on-site, full-time position for candidates with hands-on machine learning experience, working closely with engineers across computer vision, embedded systems, software, and UI/UX to integrate AI pipelines into real-time systems.

Key Responsibilities

Design and implement machine learning pipelines that encode visual inputs such as pose, face, and object/classification signals into shared embedding spaces for similarity and predictive tasks.
Build and fine-tune convolutional and transformer-based neural architectures for visual recognition and representation learning.
Develop encoding and embedding techniques that support consistent comparison across multiple data types (including pose vectors, facial landmarks, and class labels).
Use methods such as cosine similarity, distance metrics, and latent clustering for behavioral inference and action prediction.
Support model training, evaluation, and deployment workflows, including data preprocessing and augmentation, hyperparameter tuning, and performance profiling.
Collaborate with engineers in computer vision, embedded systems, software, and UI/UX to ensure AI pipelines integrate smoothly into real-time systems.
Write clean, well-documented code, and maintain version-controlled model artifacts and experiment logs.
Create technical documentation covering models, training procedures, evaluation criteria, and system integration.

Required Qualifications

Bachelor’s or Master’s degree in Artificial Intelligence, Data Science, Computer Science, Machine Learning, or a closely related field.
2 to 3 years of machine learning experience through internships, academic labs, or early career positions.
Strong understanding of Convolutional Neural Networks (CNNs) for image and video-based tasks.
Strong understanding of transformer architectures for vision or multimodal learning.
Strong understanding of embedding systems and vector space modeling for semantic and similarity-based tasks.
Strong understanding of encoding mechanisms and dimensionality reduction for latent representations.
Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
Familiarity with pose estimation, facial recognition, or classification models (for example: OpenPose, MediaPipe, FaceNet, and ResNet variants).
Experience training models using structured and unstructured visual datasets.
Exposure to cosine similarity, triplet loss, contrastive learning, or temporal prediction modeling.
Strong computer science fundamentals, including data structures, algorithms, and software design patterns.
Comfort working in Linux-based development environments and using version control systems such as Git.
Collaborative mindset with excellent communication skills and willingness to learn across domains.

Technologies

Python, PyTorch, TensorFlow
Git
OpenPose, MediaPipe, FaceNet, ResNet
ONNX, TensorRT
MLflow, Weights & Biases, DVC
CLIP, DINO

Benefits

Comprehensive health coverage
Flexible PTO
A collaborative and intellectually driven team environment
Opportunity to work on cutting-edge AI systems supporting mission-critical applications

Bonus (Nice-to-Have)

Experience integrating vision-based AI models into embedded or robotics systems.
Familiarity with ONNX or TensorRT for model optimization and deployment.
Background in sequence modeling, recurrent architectures, or video-based action recognition.
Exposure to multimodal AI systems that blend image, pose, and metadata representations.
Familiarity with CLIP, DINO, or self-supervised representation learning.
Experience with MLOps or training orchestration tools such as MLflow, Weights & Biases, or DVC.

Additional Information

Location: Santa Monica, CA (on-site)
Contract type: Full-time
Compensation: USD 100,000 to 120,000 per year
Experience level: 1–2 years (as listed)
Must be a US Citizen or valid Green Card holder; visa sponsorship is not available.
Candidates must reside within commutable distance of Santa Monica, California.
Benefits include comprehensive health coverage and flexible PTO.

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1mo ago

Lead Software Engineer - Fullstack Java/AWS/AI/ML

JPMorganChase Plano, TX

Plano, TX

1mo ago Closed

Full time Backend Developer AI API

This position is no longer accepting applications (closed Jul 21, 2026).

Job Description

Lead Software Engineer - Fullstack Java/AWS/AI/ML at JPMorganChase in Plano, TX (onsite) focused on delivering secure, scalable technology across frontend, backend, AI/ML, and data pipelines.

Responsibilities

Design, implement, and troubleshoot innovative software solutions as a core contributor on an agile team, approaching problems beyond conventional methods to deliver effective outcomes.
Champion enterprise AI assisted engineering practices to elevate code quality, delivery speed, and operational outcomes, including AI assisted code review, refactoring, test strategy acceleration, and incident analysis support; establish consistent validation standards (secure coding, peer review, automated testing) and promote reusable patterns.
Leverage SDLC tools, including AI assisted development and automation capabilities, to maximize the value delivered by automation.
Full stack development: build modern frontend applications with JavaScript frameworks (React, Angular, or Vue.js), develop robust backend services in Java and Python, and ensure seamless integration across UI layers, APIs, middleware, and data stores.
AI/ML model development and edge deployment: train, fine tune, and optimize models using PyTorch, TensorFlow, Hugging Face, ONNX, and TensorRT; package for edge device inference and cloud backends; manage the full lifecycle from experimentation to monitoring.
Cloud infrastructure and operations on AWS: provision and manage cloud resources for data pipelines and model serving using EC2, S3, Lambda, ECS/EKS, SageMaker, Kinesis, IAM, CloudWatch; uphold operational excellence through monitoring, alerts, cost optimization, and infrastructure as code.
Data pipeline development and integration: design scalable, fault tolerant pipelines to ingest, transform, and deliver data across distributed systems; architect event driven and streaming data flows with Apache Kafka and other messaging systems to support real time processing.
Collaboration and technical leadership: partner with data scientists, product managers, and platform teams to translate business requirements into technical solutions; participate in architecture and code reviews and contribute to engineering best practices and standards.

Requirements

Formal training or certification in software engineering concepts plus 5+ years of applied experience.
Proven experience leading effective use of approved AI assisted software development tools, with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs and outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.
Programming languages and frameworks: proficiency in JavaScript/TypeScript (with modern front end frameworks), Java (Spring Boot or similar), and Python; demonstrated ability to work across all three ecosystems.
Data streaming and messaging: extensive experience with Kafka (producers, consumers, Kafka Streams, Connect, Schema Registry) and familiarity with RabbitMQ, AWS SQS/SNS.
AI/ML model development: hands on experience building and training models with PyTorch, TensorFlow, or JAX; familiarity with model optimization and edge deployment tools (ONNX Runtime, TensorRT, TFLite, Core ML); experience with experiment tracking tools such as MLflow or Weights & Biases.
AWS cloud operations: hands-on experience operating production workloads on AWS, including provisioning infrastructure, managing deployments, troubleshooting issues, and implementing CI/CD pipelines in a cloud native environment.
Data engineering: experience building ETL/ELT pipelines and working with structured and unstructured data at scale; familiarity with Apache Spark, Airflow, or Step Functions is a plus.
Software engineering practices: solid foundation in Git version control, containerization, orchestration, automated testing, and CI/CD tooling.