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Embedded Ai Engineer Jobs in New Rochelle, NY (NOW HIRING)

Visa Hunt

Applied AI Engineer

Manhattan, NY · On-site

$120 - $190/hr

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction ... Develop and improve engineering processes, tools, and systems to scale AI solutions across Ramp

New

Turing

Forward Deployed AI Engineer

New York, NY

... AI deployments. In this role, you'll partner directly with customers to design, build, and deploy ... This is a hands-on engineering role embedded within customer projects-ideal for engineers who enjoy ...

Ramp

Applied AI Engineer

New York, NY · On-site

$204K - $352K/yr

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction ... Develop and improve engineering processes, tools, and systems to scale AI solutions across Ramp

Dechert LLP

AI Engineer - New York

Manhattan, NY · On-site

$140 - $175/hr

The AI Engineer is embedded within the Dechert Innovation Laband works directly with attorneys, legal professionals, and business-servicesteams to discover, evaluate, prototype, and pilot emerging AI ...

New

DeepIntent

Applied AI Engineer

New York, NY · On-site

$105K - $120K/yr

Serve as the technical point of contact embedded within commercial and PMO teams, building strong ... Engineering * Hands-on experience with AI/LLM tools, including configuring and prompting models ...

Traversal

AI Engineer - Product Engineer

Manhattan, NY · On-site

$150 - $300/hr

Our roots remain deeply embedded in AI research, and we're channeling that scientific rigor and ... The Role As a Product Engineer at Traversal, you'll play a key role in designing and building core ...

Traversal

AI Engineer - Product Engineer

New York, NY · On-site

$150K - $300K/yr

Our roots remain deeply embedded in AI research, and we're channeling that scientific rigor and ... As a Product Engineer on the Agents team, you'll own how that agent behaves, where it shows up, and ...

Percepta

Forward Deployed AI Engineer

Manhattan, NY · On-site

$150 - $230/hr

Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers ...

OpenAI

Applied AI Engineer, Digital Natives

New York, NY · On-site

$197K - $278K/yr

Deeply embedded with our most sophisticated and technical platform customers, serving as their ... Scale the Applied AI Engineer function through sharing knowledge, codifying best practices, and ...

Traversal

AI Engineer - Cloud Infrastructure

New York, NY · On-site

$175K - $275K/yr

Our roots remain deeply embedded in AI research, and we're channeling that scientific rigor and ... The Role As an AI Engineer - Cloud Infrastructure on Traversal's Infrastructure team, you'll design ...

Traversal

AI Engineer - Cloud Infrastructure

Manhattan, NY · On-site

$175 - $275/hr

Our roots remain deeply embedded in AI research, and we're channeling that scientific rigor and ... The Role As an AI Engineer - Cloud Infrastructure on Traversal's Infrastructure team, you'll design ...

Turing

Forward Deployed AI Engineer

New York, NY · On-site

For enterprises, Turing builds proprietary intelligence systems that integrate AI into mission ... This is a hands-on engineering role embedded within customer projects-ideal for engineers who enjoy ...

Norbert Health

Computer Vision/ML Engineer

New York, NY · On-site

$122K - $143K/yr

The position We are looking for our lead deep learning engineer to spearhead the development of our ... Cutting-edge stack: embedded AI, robotics, LLMs, multimodal sensing * Talented, international team ...

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Showing results 41-60

All JobsEmbedded Ai Engineer JobsEmbedded Ai Engineer Jobs in New Rochelle, NY

Embedded Ai Engineer information

See New Rochelle, NY salary details

$72K

$157.8K

$179.1K

How much do embedded ai engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for embedded ai engineer in New Rochelle, NY is $157,841.00, according to ZipRecruiter salary data. Most workers in this role earn between $135,300.00 and $178,000.00 per year, depending on experience, location, and employer.

What is an embedded AI engineer?

An Embedded AI Engineer is a professional who designs, develops, and implements artificial intelligence (AI) algorithms and models directly onto embedded systems, such as microcontrollers or edge devices. Their work involves optimizing AI solutions to run efficiently on hardware with limited computing resources, power, and memory. They collaborate with hardware engineers and software developers to integrate machine learning, computer vision, or other AI functionalities into products like smart appliances, autonomous vehicles, or IoT devices. Their expertise helps bring intelligent features directly to devices, enabling real-time decision-making without needing constant cloud connectivity.

What are the key skills and qualifications needed to thrive as an embedded AI engineer?

To thrive as an Embedded AI Engineer, you need expertise in embedded systems, AI/ML algorithms, programming languages like C/C++ and Python, and typically a degree in computer engineering or a related field. Familiarity with development tools such as TensorFlow Lite, ONNX, embedded Linux, and microcontroller platforms is essential, along with experience deploying AI models on resource-constrained devices. Strong problem-solving, collaboration, and communication skills help you work effectively in multidisciplinary teams and address real-world challenges. These skills ensure efficient integration of AI into embedded systems, enabling innovative, high-performance solutions for edge computing.

How does an embedded AI engineer typically collaborate with hardware and software teams during a project?

Embedded AI Engineers work closely with both hardware and software teams to ensure AI models are efficiently integrated into resource-constrained devices. They often collaborate with hardware engineers to optimize model performance based on device limitations like memory and processing power. At the same time, they coordinate with software developers to design efficient firmware and manage data pipelines. Regular cross-functional meetings and code reviews are common to address integration challenges and maintain alignment throughout the project lifecycle.

What is the difference between Embedded Ai Engineer vs Machine Learning Engineer?

CriteriaEmbedded Ai EngineerMachine Learning Engineer
Required CredentialsBachelor's in Electrical Engineering, Computer Science, or related; knowledge of embedded systemsBachelor's or Master's in Computer Science, Data Science, or related; strong programming skills
Work EnvironmentEmbedded systems, IoT devices, hardware integrationData centers, cloud platforms, software development environments
Employer & Industry UsageConsumer electronics, automotive, IoT companiesTech firms, startups, research institutions
Common Search & ComparisonYesNo

Embedded Ai Engineers focus on integrating AI algorithms into embedded hardware and IoT devices, requiring knowledge of hardware constraints and embedded programming. Machine Learning Engineers develop models primarily for software applications and data analysis. While both roles involve AI, Embedded Ai Engineers specialize in hardware-software integration within embedded systems, whereas Machine Learning Engineers work on developing and deploying AI models in software environments.

What are popular job titles related to Embedded Ai Engineer jobs in New Rochelle, NY?

For Embedded Ai Engineer jobs in New Rochelle, NY, the most frequently searched job titles are:

What job categories do people searching Embedded Ai Engineer jobs in New Rochelle, NY look for?

The top searched job categories for Embedded Ai Engineer jobs in New Rochelle, NY are:

What cities near New Rochelle, NY are hiring for Embedded Ai Engineer jobs?

Cities near New Rochelle, NY with the most Embedded Ai Engineer job openings:

Embedded Ai Engineer jobs near you

AI Field Engineer - AI / ML & Agentic Systems

MetaSense, Inc.

Manhattan, NY • On-site

Other

Posted 2 days ago

New

Job description

Title: AI Field Engineer – AI / ML & Agentic Systems

Location: Remote, New York, NY, or San Mateo, CA
Duration: Full Time (including OPT and H-1B transfers)
Interview Mode: Video + Onsite
 
About the Role
We are looking for an experienced AI Field Engineer who combines strong AI/ML engineering skills with customer-facing technical expertise.
This role sits at the intersection of engineering, product, pre-sales, and customer delivery. You will work directly with customers to understand complex AI requirements, build proofs of concept, develop MVPs, integrate AI systems into production environments, and help customers successfully deploy and optimize AI solutions.
The ideal candidate is highly technical, comfortable writing production code, and equally confident presenting technical solutions to engineering teams, product leaders, and senior stakeholders.
 
Key Responsibilities
  • Work directly with customers to understand technical and business requirements.
  • Build and deliver Proofs of Concept (POCs) and MVPs.
  • Develop production-ready AI/ML integrations.
  • Embed and deploy AI solutions within customer environments.
  • Own customer-facing technical implementations from discovery through production.
  • Build, maintain, and optimize ML/AI systems.
  • Support AI model deployment, inference, training, and fine-tuning workflows.
  • Help customers optimize application and model performance.
  • Manage technical relationships with customer accounts.
  • Present architecture, strategy, technical trade-offs, and business outcomes to stakeholders.
  • Translate customer feedback into product and engineering improvements.
  • Collaborate closely with product teams to rapidly improve solutions based on customer needs.
  • Support enterprise and AI-native customers through fast-moving implementation cycles.
  • Take significant ownership of technical delivery and customer outcomes.
Required Technical Skills
Candidates should have strong experience with:
  • Python
  • Machine Learning
  • Artificial Intelligence
  • Large Language Models (LLMs)
  • Generative AI
  • Production ML / AI systems
  • LLM deployment
  • Model fine-tuning
  • Model training
  • Inference optimization
  • Cloud infrastructure
  • Production system integration
  • Customer-facing technical implementation
LLM / GenAI Skills
Strong preference is given to candidates with hands-on experience in:
  • Supervised Fine-Tuning (SFT)
  • Direct Preference Optimization (DPO)
  • Reinforcement Fine-Tuning (RFT) or equivalent methods
  • LLM training and fine-tuning
  • Production LLM deployment
  • GenAI infrastructure
  • Inference optimization
  • Open-source models
Experience should go beyond theoretical knowledge or advisory work and include actual implementation and deployment into production environments.
 
Infrastructure & Deployment Skills
Relevant experience includes:
  • AWS
  • Google Cloud Platform
  • Azure
  • GPU infrastructure
  • Kubernetes
  • Model serving
  • AI/ML infrastructure
  • Cloud deployment
Experience with inference-serving frameworks such as:
  • vLLM
  • SGLang
is highly valuable.
 
Client-Facing / Pre-Sales Experience
This role requires genuine customer-facing technical experience.
Candidates should have experience with activities such as:
  • Running technical discovery sessions
  • POCs and Proofs of Value
  • MVP development
  • Technical workshops
  • Pre-sales engineering
  • Solution architecture
  • Customer implementation
  • Account technical management
  • Presenting to technical and executive stakeholders
  • Embedding code into customer environments
  • Owning technical customer relationships
Candidates must combine both AI/ML technical depth and client-facing experience.
Eligibility Criteria
Candidates should have:
  • 3–10 years of relevant professional experience.
  • Preferably 5+ years for senior profiles.
  • Experience in a customer-facing technical AI/ML role.
  • Strong software engineering ability.
  • Strong Python skills.
  • Proven experience building and shipping production AI/ML systems.
  • Experience developing systems from the ground up.
  • Direct experience running POCs or MVPs.
  • Experience presenting technical solutions to stakeholders.
  • Strong understanding of LLMs and GenAI systems.
  • Experience with model deployment, fine-tuning, training, or inference.
  • Ability to manage customer relationships.
  • Ability to independently own complex technical implementations.
  • Strong communication and presentation skills.
  • Ability to operate effectively in fast-paced environments.
  • Comfort with regular on-site customer visits within the U.S.
Relevant Candidate Backgrounds
Suitable backgrounds may include:
  • Forward-Deployed Engineer
  • AI Field Engineer
  • Solutions Architect
  • Sales Engineer
  • Applied AI Engineer
  • Machine Learning Engineer
  • ML Infrastructure Engineer
  • AI Infrastructure Engineer
  • Customer Success Engineer with strong technical depth
  • Client-facing AI Engineer
  • Technical Account Manager with hands-on AI engineering experience
Preferred Candidate Archetypes
Profile A – Forward-Deployed / Embedded AI Engineer
Candidates who have:
  • Worked directly with customers.
  • Built AI solutions inside customer environments.
  • Owned POCs and production implementations.
  • Worked at AI-native or high-growth technology environments.
  • Strongly combined engineering with customer delivery.
Profile B – Senior ML / AI Engineer
Candidates who have:
  • Strong ML/AI engineering depth.
  • Experience with model training, fine-tuning, inference, or deployment.
  • Built production AI systems.
  • Also demonstrated meaningful customer-facing or pre-sales experience.
Ideal Candidate Profile
The ideal candidate should:
  • Have strong hands-on engineering ability.
  • Be highly customer-focused.
  • Demonstrate high ownership.
  • Have a low-ego working style.
  • Learn new technologies quickly.
  • Be comfortable working independently.
  • Be able to move rapidly from problem definition to implementation.
  • Be comfortable switching between coding and customer conversations.
  • Understand both technical architecture and business outcomes.
  • Be capable of communicating with both engineers and executives.
  • Have strong product thinking.
  • Turn customer feedback into concrete product improvements.
  • Thrive in fast-paced and ambiguous environments.
Strong Candidate Signals
Recruiters should prioritize candidates who demonstrate:
  • Production LLM deployment.
  • Hands-on AI-native product experience.
  • Strong Python.
  • Model fine-tuning or training.
  • Inference optimization.
  • Cloud GPU infrastructure.
  • POCs and MVP ownership.
  • Direct customer-facing engineering.
  • Production implementation inside customer environments.
  • Strong technical presentation skills.
  • Experience managing technical customer relationships.
  • Consistent full-time employment history.
  • Meaningful ownership over AI/ML systems.
Profiles Less Aligned With the Role
Candidates may be less suitable if they have:
  • Pure Solutions Architect or advisory experience without hands-on production coding.
  • No experience shipping code inside customer environments.
  • AI experience limited to consulting or strategy.
  • No production LLM experience.
  • No experience with GenAI features or open-source models.
  • Strong AI experience but no customer-facing exposure.
  • Strong pre-sales experience but limited AI/ML technical depth.
  • Traditional professional-services backgrounds without meaningful AI/ML product experience.
  • Traditional banking or insurance backgrounds without relevant AI/ML engineering experience.
  • Pure Big Tech individual-contributor experience with no external/customer exposure.
  • Multiple employment tenures shorter than one year.
  • Primarily contract-based experience without consistent full-time employment.
  • Limited U.S.-based technology-industry experience.
Key Screening Areas
Candidates should be able to clearly explain:
  • An AI/ML system they personally built and shipped to production.
  • Their specific technical contribution to the project.
  • A POC or MVP they delivered for a customer.
  • Experience embedding software or AI solutions into customer environments.
  • Direct customer-facing responsibilities.
  • Experience managing technical accounts.
  • Python expertise.
  • LLM deployment experience.
  • Model training or fine-tuning experience.
  • SFT, DPO, RFT, or related methodologies.
  • Inference optimization experience.
  • Experience with vLLM, SGLang, or similar frameworks.
  • AWS, Google Cloud Platform, or Azure experience.
  • Kubernetes experience.
  • Experience handling executive or senior stakeholder conversations.
  • Examples of translating customer feedback into product improvements.
  • Willingness to travel regularly to customer locations.
Interview Process
1.    Candidate Submission  The candidate profile is submitted for review. If the hiring team finds the profile suitable, the candidate moves to the assessment stage.
 
2.     Take-Home Assignment – Self-Paced  Candidates build a working Text-to-SQL system. They are expected to:
  • Take provided database table schemas.
  • Generate correct SQL queries.
  • Validate that the generated queries return the correct results.
  • Submit working code along with evidence that the solution functions correctly.
3.     Evaluation focuses on:  
  • Correctness
  • Clean and readable code
  • Logical project structure
  • Error handling
  • Testing and validation
  • Ability to demonstrate that the system works reliably
4.     Recruiter Screen – 30 Minutes  Initial conversation covering:
  • Candidate logistics and availability
  • Motivation for considering the role
  • Relevant experience
  • Overall role fit  
  • Career expectations
5.     Discovery + Hiring Manager Interview – 45 Minutes  Candidates participate in a live customer-discovery role-play.  This round evaluates:
  • Client-facing communication
  • Discovery and requirement-gathering skills
  • Ability to ask effective technical questions
  • Understanding of customer problems
  • Ability to translate requirements into technical solutions
  • Technical and commercial communication
6.     Culture + Live Coding – 60 Minutes This round combines a discussion with a hands-on technical assessment.  Product & Production Discussion
  • Product thinking
  • Production AI/ML systems
  • Engineering decision-making
  • Customer and business considerations
7.     Live Coding
  • Candidates extend or modify their take-home solution live.
  • Evaluation focuses on coding ability, problem-solving, code quality, adaptability, and technical reasoning.
8.     On-Site Final Loop – Approximately 2 Hours  The final loop includes:  Customer Demo / Presentation
  • Present a technical solution clearly.
  • Explain architecture and technical decisions.
  • Communicate effectively with customer-facing stakeholders.
  • Demonstrate both technical depth and presentation ability.
9.     Values / Leadership Conversation
  • Ownership
  • Working style
  • Collaboration

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