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Senior Embedded Machine Learning Jobs in New York

NxT Level

Senior Machine Learning Engineer

Manhattan, NY

$133K - $176K/yr

Our client is a public safety product that's looking to add a machine learning engineer to their team. This particular role is focused on NLP/NLU for their real-time audio translation team. If you're ...

Shaped

Senior Machine Learning Engineer

Brooklyn, NY · On-site

$130K - $200K/yr

Ultimately, our goal is to make AI accessible and we see machine-learning for discovery (e.g. TikTok "For-You Page", FB "News feed" and YouTube "What to watch next") as the best place to start.

CVS Health

Senior Machine Learning Engineer

New York, NY · On-site

$111K - $222K/yr

We're looking for a Senior Machine Learning Engineer to help build and scale the next generation of data science and AI products in the journey. In this role, you'll leverage your engineering ...

Career Renew

Senior Machine Learning Engineer

New York, NY · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ...

adMarketplace

Senior Machine Learning Engineer

Manhattan, NY · On-site

$114K - $157K/yr

The Role We are seeking an experienced Senior Machine Learning Engineer passionate about building impactful products in the search and advertising technology ecosystem. As part of our established AI ...

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Senior Embedded Machine Learning information

What is the difference between Senior Embedded Machine Learning vs Embedded Software Engineer?

AspectSenior Embedded Machine LearningEmbedded Software Engineer
Required CredentialsBachelor's/Master's in CS, EE, or related; experience in ML and embedded systemsBachelor's in CS, EE, or related; strong programming skills in C/C++
Work EnvironmentDeveloping ML models for embedded devices, hardware integrationDesigning and implementing embedded software for devices
Industry UsageAI/ML-focused companies, IoT, consumer electronicsAutomotive, industrial, consumer electronics

While both roles involve embedded systems, Senior Embedded Machine Learning focuses on integrating ML models into hardware, requiring knowledge of AI and data science. Embedded Software Engineers primarily develop software for embedded devices, emphasizing firmware and system-level programming. The roles overlap in embedded environment skills but differ in their core focus on AI versus traditional software development.

What are some common challenges faced by Senior Embedded Machine Learning Engineers when deploying models on edge devices?

Senior Embedded Machine Learning Engineers often encounter challenges such as optimizing model size and inference speed to fit within the limited computational resources and memory of edge devices. Balancing accuracy and performance while minimizing power consumption is critical, especially for battery-operated products. Additionally, integrating models with existing embedded software and ensuring reliable, real-time operation can require close collaboration with hardware and firmware teams. Staying current with advancements in model compression and hardware acceleration is also essential for success in this role.

What are the key skills and qualifications needed to thrive as a Senior Embedded Machine Learning Engineer, and why are they important?

To thrive as a Senior Embedded Machine Learning Engineer, you need expertise in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often backed by an advanced degree in computer science or electrical engineering. Familiarity with tools such as TensorFlow Lite, ONNX, and embedded hardware platforms (e.g., ARM Cortex-M, NVIDIA Jetson) is typically required. Strong problem-solving, project management, and communication skills distinguish top performers in this role. These capabilities are crucial for efficiently deploying optimized machine learning models on resource-constrained devices and effectively collaborating across multidisciplinary teams.

What does a Senior Embedded Machine Learning engineer do?

A Senior Embedded Machine Learning engineer designs, develops, and optimizes machine learning models to run efficiently on resource-constrained embedded devices such as microcontrollers, IoT devices, and edge hardware. They are responsible for integrating ML algorithms with embedded systems, ensuring low latency and minimal power consumption. Their work often involves collaborating with hardware engineers and software developers to deploy intelligent features in products like smart sensors, wearables, and autonomous systems.
What are the most commonly searched types of Embedded Machine Learning jobs in New York? The most popular types of Embedded Machine Learning jobs in New York are:
What cities in New York are hiring for Senior Embedded Machine Learning jobs? Cities in New York with the most Senior Embedded Machine Learning job openings:
Senior Embedded Machine Learning jobs near you
Senior Machine Learning Engineer

Senior Machine Learning Engineer

NxT Level

Manhattan, NY

$133K - $176K/yr

Other

Posted 19 days ago

Job description

Our client is a public safety product that's looking to add a machine learning engineer to their team. This particular role is focused on NLP/NLU for their real-time audio translation team. 

If you're excited about the future of video, audio, video content moderation, and more then you should reach out.

Core Challenges: 

  • Mobile Video Streaming: The app is designed to ingest high-quality, low-latency video, transcode, and redistribute it to external media seamlessly.
  • Radio Hardware: Development of proprietary software-defined radio-based devices to capture all radio dispatches in every major city, covering both analog and digital signals.

Preferred Qualifications

  • A degree in Computer Science, Machine Learning, or a related field, or equivalent professional experience.
  • A strong theoretical foundation in core Machine Learning concepts and techniques.
  • Proficiency in fundamental principles of Linear Algebra, Statistics, and Probability.
  • Experience with various ML techniques and frameworks, such as data discretization, normalization, sampling, linear regression, decision trees, SVMs, and deep neural networks.
  • Familiarity with DL software frameworks like TensorFlow or PyTorch.
  • At least 3 years of experience leading and implementing effective ML solutions in large scale production environments.

Technology Environment: Go for Transactional Systems and Python for Analytic Systems running on a Kubernetes Infrastructure in GCP. Database is in Cassandra, MySQL, Redis, and Google PubSub. Web Services are build via React.js and Typescript.

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