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Deep Learning Accelerator Jobs in New York (NOW HIRING)

Agilisium

GenAI Architect

Jersey City, NJ · On-site

$65.50 - $86.50/hr

Enable reuse of AI components and accelerators across engagements. Mentorship & Collaboration ... Strong foundation in AI/ML algorithms, deep learning, NLP, computer vision, and LLMs (e.g., GPT ...

Meta

Software Engineer, Systems ML

New York, NY · On-site

$183K/yr

Specialized experience in one or more of the following machine learning/deep learning domains: Hardware accelerators architecture, GPU architecture, machine learning compilers, or ML systems, AI ...

Genpact LLC

Sr. Data Scientist 4B

Port Reading, NJ · On-site

Genpact's AI Gigafactory, our industry-first accelerator, exemplifies how we scale advanced ... machine learning techniques to develop innovative AI solutions. This position requires a deep ...

Genpact

Prinicipal - Industry Advisory

New York, NY · On-site

Genpact's AI Gigafactory, our industry-first accelerator, exemplifies how we scale advanced ... Deep Learning, Generative AI, Healthcare Analytics, Insurance, Legacy Modernization, Machine ...

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Deep Learning Accelerator information

What is a deep learning accelerator?

Deep Learning Accelerators are specialized hardware or systems designed to speed up the processing and training of deep learning algorithms, such as neural networks. They are optimized for the heavy computational demands of tasks like image recognition, natural language processing, and other AI applications. Examples include Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and custom-designed chips like Application-Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs). These accelerators enable faster data processing, lower power consumption, and improved efficiency compared to general-purpose CPUs. As AI applications grow, the use of deep learning accelerators is becoming increasingly important in both research and industry.

What skills and qualifications are needed to thrive as a deep learning accelerator engineer?

To thrive as a Deep Learning Accelerator Engineer, you need a strong background in computer engineering, digital design, and machine learning, typically supported by a degree in computer science or electrical engineering. Experience with hardware description languages (such as Verilog or VHDL), FPGA/ASIC toolchains, and familiarity with deep learning frameworks like TensorFlow or PyTorch is essential. Problem-solving, teamwork, and effective communication are crucial soft skills for collaborating with cross-functional teams and translating algorithmic requirements into efficient hardware solutions. These skills are vital to designing high-performance, energy-efficient hardware accelerators that advance AI capabilities and meet industry demands.

What are the main challenges faced when optimizing deep learning models for hardware accelerators?

One of the primary challenges in this role is bridging the gap between deep learning model requirements and the constraints of specialized hardware, such as GPUs, TPUs, or custom ASICs. This often involves model quantization, memory optimization, and adapting algorithms to exploit hardware parallelism while maintaining accuracy and efficiency. Collaboration with both hardware engineers and software developers is essential to ensure models run efficiently on target platforms, and staying current with evolving accelerator architectures is key to long-term success.

What is the difference between Deep Learning Accelerator vs Machine Learning Engineer?

AspectDeep Learning AcceleratorMachine Learning Engineer
Required CredentialsKnowledge of hardware design, FPGA/ASIC programming, deep learning frameworksDegree in Computer Science, Data Science, or related fields; experience with ML frameworks
Work EnvironmentHardware development labs, embedded systems, AI hardware companiesSoftware development environments, tech companies, research labs
Industry UsageAI hardware manufacturing, embedded AI solutionsAI/ML software development, data analysis, model deployment
Search & Comparison IntentFocus on hardware acceleration, AI hardware designFocus on software development, model building

Deep Learning Accelerators specialize in hardware design and optimization for AI workloads, working closely with hardware and embedded systems. Machine Learning Engineers develop and deploy ML models primarily through software, focusing on algorithms and data. While both roles involve AI, their core skills, work environments, and industry applications differ significantly.

What cities in New York are hiring for Deep Learning Accelerator jobs?

Cities in New York with the most Deep Learning Accelerator job openings:

Deep Learning Accelerator jobs near you

GenAI Architect

Jersey City, NJ • On-site

Agilisium
501 - 1,000 employees
Learn more about Agilisiumexternal

$65.50 - $86.50/hr

Full-time

Re-posted 7 days ago

Job description


Job Summary:
We are seeking a visionary AI Architect to lead the design, development, and evangelism of cutting-edge AI solutions. This role blends technical depth with client-facing leadership, driving AI strategy, pre-sales efforts, PoCs, and AI innovation initiatives. The ideal candidate is a seasoned AI professional who combines architectural expertise, thought leadership, and the ability to translate complex AI capabilities into real- world business impact for clients.
Key Responsibilities:
AI Strategy & Thought Leadership
  • Act as a thought leader in AI/ML representing the organization at industry forums, client workshops, and strategic events.
  • Collaborate with leadership to shape the AI vision and roadmap aligned to business and market needs.
  • Stay ahead of emerging AI trends (GenAI, LLMs, Computer Vision, NLP, MLOps) and bring innovative ideas to the table.

Client Engagement & Advisory
  • Serve as a trusted advisor to C-level clients, understanding their business challenges and recommending AI-powered solutions.
  • Lead client workshops, discovery sessions, and strategy engagements to articulate AI value propositions.
  • Build strategic relationships with key enterprise clients and influence AI adoption across business lines.

AI Solution Architecture
  • Architect scalable and production-grade AI solutions across supervised/unsupervised learning, NLP, CV, and Generative AI.
  • Design end-to-end AI solution architectures - data ingestion, model development, MLOps, deployment, and monitoring.
  • Define solution blueprints, technology stack, and reference architectures tailored to client needs.

Pre-Sales & Solutioning
  • Partner with sales and delivery teams to craft compelling AI proposals and presentations.
  • Lead technical solutioning, estimation, and responses for RFPs/RFIs and client bids.
  • Translate business problems into technical solutions, articulating ROI and competitive differentiation.

PoC Development & Innovation
  • Drive rapid development of AI Proof-of-Concepts and pilot solutions to demonstrate feasibility and business impact.
  • Lead agile cross-functional teams (data scientists, engineers, SMEs) to deliver high-quality prototypes.
  • Enable reuse of AI components and accelerators across engagements.

Mentorship & Collaboration
  • Mentor internal teams (data scientists, engineers) on AI best practices and career growth.
  • Collaborate with product, engineering, and domain teams to scale AI across platforms and services.

Required Skills & Experience:
Technical Expertise
  • Strong foundation in AI/ML algorithms, deep learning, NLP, computer vision, and LLMs (e.g., GPT, BERT, T5).
  • Experience in deploying models using cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
  • Proficiency in Python, TensorFlow/PyTorch, Scikit-learn, LangChain, Hugging Face, or equivalent frameworks.
  • Understanding of MLOps, CI/CD for ML, model monitoring, and governance.

Strategic & Client-Facing Skills
  • Proven experience in client consulting, pre-sales, or solutioning roles in AI services or platforms.
  • Excellent communication and storytelling skills with the ability to influence stakeholders at various levels.
  • Experience working across verticals such as Healthcare, BFSI, Retail, Manufacturing, or Life Sciences is a plus.

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