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Deep Learning Accelerator Jobs in Florida (NOW HIRING)

Cyber AI Security Manager

Sarasota, FL · On-site +1

$107K - $144K/yr

You'll have the time, space, and support to go deep in your projects and build lasting technical ... Act as the security expert in designing, developing, and deploying secure AI and machine learning ...

Cyber AI Security Manager

Tampa, FL · On-site +1

$104K - $141K/yr

You'll have the time, space, and support to go deep in your projects and build lasting technical ... Act as the security expert in designing, developing, and deploying secure AI and machine learning ...

Cyber AI Security Manager

Fort Lauderdale, FL · On-site +1

$106K - $143K/yr

You'll have the time, space, and support to go deep in your projects and build lasting technical ... Act as the security expert in designing, developing, and deploying secure AI and machine learning ...

Cyber AI Security Manager

Tallahassee, FL · On-site +1

$105K - $142K/yr

You'll have the time, space, and support to go deep in your projects and build lasting technical ... Act as the security expert in designing, developing, and deploying secure AI and machine learning ...

Cyber AI Security Manager

Miami, FL · On-site +1

$106K - $143K/yr

You'll have the time, space, and support to go deep in your projects and build lasting technical ... Act as the security expert in designing, developing, and deploying secure AI and machine learning ...

Showing results 21-27

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.

Infographic showing various Deep Learning Accelerator job openings in Florida as of June 2026, with employment types broken down into 2% Internship, 5% As Needed, 81% Full Time, 10% Part Time, and 2% Summer. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

AIA - Service Line Sales Specialist

Orlando, FL • On-site

Full-time

Re-posted 23 hours ago


Key responsibilities

  • Lead discovery discussions with senior client stakeholders to identify sales, service, and marketing objectives and translate them into solution roadmaps.

  • Define and develop comprehensive service line solution offerings that combine consulting methods, platforms, and accelerators to deliver value to clients.

  • Review and refine proposals for strategic opportunities, shaping scope, effort estimates, and value cases to ensure commercial viability and operational feasibility.


Cognizant rating

7.0

Company rating: 7.0 out of 10

Based on 87 frontline employees who took The Breakroom Quiz


Job description

AIA - Artificial Intelligence & Analytics - Service Line Sales Specialist
Location: Remote/ living in San Francisco, Foster City CA with travel to SoCa Los Angeles Bay area.
Job Summary
Serve as a senior service line specialist focused on sales service and marketing transformation using deep domain expertise to shape complex digital solutions for global clients in a hybrid work model driving revenue growth customer experience excellence and sustainable long-term value across diverse industries through advisory solution definition and delivery oversight.
Responsibilities
  • Lead discovery discussions with senior client stakeholders to capture nuanced sales service and marketing objectives that can be translated into practical solution roadmaps aligned with measurable business value targets in revenue growth and customer satisfaction metrics.
  • Define comprehensive service line solution offerings for sales service and marketing domains that combine consulting methods platforms and accelerators into clear value propositions that can be adopted efficiently by diverse multinational clients.
  • Develop detailed business process models across lead management opportunity management customer onboarding case handling and campaign execution that streamline handoffs between sales service and marketing teams while improving overall customer experience.
  • Guide creation of domain specific solution blueprints that integrate customer relationship tools analytics and workflow applications to support omnichannel engagement scenarios across digital and assisted interaction channels.
  • Review and refine proposals for strategic opportunities by shaping scope assumptions effort estimates and value cases so that the service line offerings remain commercially viable and operationally feasible for both client and company delivery teams.
  • Coordinate with account teams to identify upsell and cross sell opportunities within existing clients by analyzing current sales and service performance and proposing targeted enhancements based on domain best practices.
  • Advise delivery teams on domain nuances during project execution so that configuration customization and integration decisions remain consistent with agreed business outcomes and industry regulations.
  • Collaborate with product and platform partners to evaluate new capabilities in sales service and marketing technologies and recommend which capabilities should be embedded into standard service line offerings.
  • Prepare and deliver compelling thought leadership content such as domain playbooks solution overviews and use case narratives that demonstrate tangible benefits of the service line to clients and internal stakeholders.
  • Enable internal teams through structured knowledge sharing sessions focused on industry trends regulatory shifts and emerging practices in customer acquisition retention and service excellence.
  • Monitor success of implemented solutions by reviewing key performance indicators for sales conversion service resolution and marketing effectiveness and recommend data driven enhancements to sustain continuous improvement.
  • Support creation of standardized methods templates and checklists that enhance repeatability and quality of sales and delivery activities within the service line and reduce time to value for clients.
  • Ensure that solutions comply with enterprise risk data privacy and ethical standards so that customer trust is protected while business goals in sales service and marketing are achieved responsibly.

Qualifications
  • Display extensive experience of working in senior functional roles across sales service or marketing environments where complex transformation programs have been executed in large enterprises.
  • Demonstrate strong understanding of end to end processes spanning customer acquisition onboarding service management loyalty programs and marketing automation within multiple industries.
  • Apply practical knowledge of leading customer relationship and marketing enablement platforms to shape realistic implementation approaches and integration patterns.
  • Use advanced analytical thinking to interpret sales pipelines service backlogs and campaign performance indicators and convert insights into prioritized improvement initiatives.
  • Communicate complex domain concepts to both technical and non technical audiences with clarity so that stakeholders can make informed decisions about solution scope and adoption.
  • Exhibit familiarity with agile and iterative delivery practices in hybrid work setups and show ability to collaborate effectively with distributed teams and client counterparts.
  • Maintain continuous learning habit regarding evolving sales service and marketing innovations ensuring that proposed solutions remain current and competitive in the global market.

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About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization's unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email [email protected] for roles based in the Americas or [email protected] for roles based in India.

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