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Ml Inference Jobs in Canton, GA (NOW HIRING)

Dolby Laboratories, Inc.

AI Architect

Atlanta, GA · On-site

Stay current with developments in AI/ML, including emerging architectures and edge inference techniques, and translate industry trends into practical, production oriented recommendations for ...

Deloitte

AI Engineer Consultant

Atlanta, GA · Hybrid

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...

Deloitte

AI Engineer Consultant

Atlanta, GA · On-site

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...

Dolby

AI Architect

Atlanta, GA · On-site

Stay current with developments in AI/ML, including emerging architectures and edge inference techniques, and translate industry trends into practical, production oriented recommendations for ...

PrizePicks

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$220K - $280K/yr

Proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference in * MLOps Expertise: Deep experience managing the full ML lifecycle (training ...

PrizePicks

Staff Machine Learning Engineer

Atlanta, GA · On-site

$220K - $280K/yr

Proficient in streaming architectures (Kafka/Flink/PubSub) and building low-latency services to serve model inference in * MLOps Expertise: Deep experience managing the full ML lifecycle (training ...

Career Renew

Senior Machine Learning Engineer

Atlanta, GA · Remote

$165K - $225K/yr

... inference on NVIDIA DGX Spark. Understanding of FDA regulatory requirements for AI/ML in medical devices Experience with MLOps tools (MLflow, Kubeflow) and model versioning practices Develop tools ...

Elevance Health

Gen AI Engineer

Atlanta, GA · Hybrid

... for inference optimization; RAG architecture design and implementation. * Advanced cloud infrastructure (AWS EKS/ECS, GCP GKE, Azure AKS) knowledge. * Containerization strategies for ML workloads;

Five and Fly

Machine Learning Engineer

Atlanta, GA · On-site

$120 - $165/hr

You will work closely with AI/ML researchers, data engineers, and product teams to design ... Hands‑on experience with LLMs/SLMs (fine‑tuning, prompt design, inference optimization)

Compunnel

Sr SQL/Oracle DBA

Alpharetta, GA · On-site

... ML model serving patterns (batch vs. real-time inference) for database-adjacent workloads Drive prompt engineering best practices for database-related AI applications

Honeywell

Sr. Director Data & AI Platform Architect

Atlanta, GA · On-site +1

$64.75 - $86.50/hr

Proven ability toarchitectend-to-end ML systems, includingdata pipelines, feature engineering ... inference optimization. US PERSON REQUIREMENTS Due to compliance with U.S. export control laws and ...

Honeywell

Sr. Director Data & AI Platform Architect

Atlanta, GA · On-site +1

$64.75 - $86.50/hr

Proven ability toarchitectend-to-end ML systems, includingdata pipelines, feature engineering ... inference optimization. US PERSON REQUIREMENTS Due to compliance with U.S. export control laws and ...

Cisco

Applied AI Scientist

Atlanta, GA · On-site

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

Honeywell

Sr Data Engineer

Atlanta, GA · On-site

$110K - $132K/yr

... ML workloads * Build and maintain data pipelines for AI product lifecycle, including training data preparation, feature engineering, and inference data flows * Develop and optimize RAG (Retrieval ...

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All JobsMl Inference JobsMl Inference Jobs in Canton, GA

Ml Inference information

See Canton, GA salary details

$35.4K

$115.9K

$185.5K

How much do ml inference jobs pay per year?

As of Aug 17, 2026, the average yearly pay for ml inference in Canton, GA is $115,888.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,000.00 and $128,400.00 per year, depending on experience, location, and employer.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.

What are popular job titles related to Ml Inference jobs in Canton, GA?

For Ml Inference jobs in Canton, GA, the most frequently searched job titles are:

What cities near Canton, GA are hiring for Ml Inference jobs?

Cities near Canton, GA with the most Ml Inference job openings:

Ml Inference jobs near you

AI Architect

Dolby Laboratories, Inc.

Atlanta, GA • On-site

Other

Re-posted 7 days ago

Job description

Join the leader in entertainment innovation and help us design the future. At Dolby, science meets art, and high tech means more than computer code. As a member of the Dolby team, you'll see and hear the results of your work everywhere, from movie theaters to smartphones. We continue to revolutionize how people create, deliver, and enjoy entertainment worldwide. To do that, we need the absolute best talent. We're big enough to give you all the resources you need, and small enough so you can make a real difference and earn recognition for your work. We offer a collegial culture, challenging projects, and excellent compensation and benefits, not to mention a Flex Work approach that is truly flexible to support where, when, and how you do your best work.
Dolby's consumer entertainment and cinema businesses are bringing Dolby's breakthrough technologies, powering the world's top movies, TV shows, music, games, and live sports to more places around the world across a wider range of consumer experiences and devices.
Role Overview
We are seeking a Senior Staff AI / Machine Learning Architect to serve as a key technical bridge between research teams and product engineering organizations. In this role, you will help translate advanced machine learning research into efficient, scalable, and production ready solutions across Dolby's product portfolio.
You will play a critical role in defining technical strategy for developing, training, and deploying AI/ML models-particularly in edge ML and NPU enabled platforms-while collaborating closely with researchers, software engineers, and external partners such as silicon vendors. This highly cross functional role combines hands on technical expertise with system level thinking and technical leadership, influencing direction across projects and teams through execution and clear technical communication.
Key Responsibilities
Technical Strategy and Leadership
  • Define and guide AI/ML technology strategy across Dolby's core technology areas (audio processing, video processing, personalization, and related domains), spanning cloud, edge, and embedded environments, with a focus on edge ML, GPUs, and NPUs
  • Anticipate evolving business and technical needs and contribute to a forward looking technical vision
  • Establish best practices, guardrails, and technical guidelines for building, training, optimizing, and deploying ML models across the organization
  • Stay current with developments in AI/ML, including emerging architectures and edge inference techniques, and translate industry trends into practical, production oriented recommendations for accelerated hardware

Bridge Research and Engineering
  • Serve as a primary technical interface between ML research teams and engineering teams
  • Define architectural approaches for integrating traditional audio/video processing (DSPs, hardware accelerators) with ML models
  • Partner with platform managers and engineering teams to integrate ML models into shipped products, and collaborate with researchers to align on requirements and constraints
  • Work with Data Engineering teams to help establish data governance guidelines and standards for data sourcing, cleaning, and pipeline management
  • Collaborate with QA teams to develop testing methodologies appropriate for AI/ML systems

Engage with Silicon Vendors
  • Develop a working understanding of GPU and NPU architectures, toolchains, operator support, and performance characteristics
  • Identify gaps between model requirements and hardware capabilities, and help drive solutions in collaboration with internal teams and external partners
  • Collaborate with and influence silicon vendors and platform partners on roadmap alignment, tooling, and hardware capabilities relevant to Dolby use cases

Hands On Technical Work
  • Conduct technical investigations and experiments, including profiling models, benchmarking inference, and evaluating accuracy latency trade offs
  • Apply and advise on model optimization techniques such as retraining, pruning, quantization, distillation, and hardware aware optimization
  • Guide model porting across frameworks and runtimes (e.g., PyTorch ONNX vendor specific runtimes)
  • Build prototypes and proof of concepts to reduce technical risk prior to full engineering investment

Qualifications
Required
  • Bachelor's or Master's degree in Electrical Engineering, Computer Science, or a related field, or equivalent practical experience
  • Significant hands on experience in AI, machine learning, and embedded software engineering (often acquired over many years of professional practice)
  • Strong software engineering skills, including experience writing production quality code and working with version control, testing, build systems, and software delivery pipelines
  • Experience with at least one major AI/ML framework (e.g., PyTorch, TensorFlow, JAX, ONNX) and the ability to learn additional frameworks as needed
  • Hands on experience deploying optimized ML models (e.g., quantization, pruning, distillation, operator fusion)
  • Experience with edge or on device ML, including awareness of constraints such as latency, power, memory, and thermal limits
  • Familiarity with CPU, GPU, NPU, and DSP architectures and their associated toolchains (e.g., Qualcomm Hexagon/QNN, MediaTek APU/NeuroPilot, ARM Ethos, Apple Neural Engine)
  • Experience in audio, video, signal processing, media codecs, or closely related technical domains

Strongly Preferred
  • Ability to work across abstraction layers, from model architecture to operator level hardware performance
  • Experience defining technical strategy and influencing cross functional teams through expertise and collaboration
  • Demonstrated experience shipping ML models to production on resource constrained devices (e.g., mobile, embedded, automotive, wearables)

Nice to Have
  • Experience with real time audio/video inference pipelines (e.g., streaming inference, causal models, latency sensitive processing)
  • Familiarity with Dolby technologies (such as Atmos, Vision, or AC 4) or comparable media standards
  • Experience with generative AI models in the audio or video domain
  • Contributions to open source ML tools or peer reviewed research

What This Role Is Not
  • This is not a pure research role; the focus is on translating research into production ready solutions
  • This is not an MLOps, LLM only, or infrastructure focused role
  • This role does not center on integrating third party APIs; the work involves developing proprietary models
  • This is not a people management role, though the position involves technical leadership and influence

The San Francisco/Bay Area base salary range for this full-time position is $152,000 - $209,000, which can vary if outside this location,plus bonus, benefits, and some roles may also include equity. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, competencies, experience, market demands, internal parity, and relevant education or training. Your recruiter can share more about the specific salary range and perks and benefits for your location during the hiring process.
#LI-JB1
Dolby will consider qualified applicants with criminal histories in a manner consistent with the requirements of San Francisco Police Code, Article 49, and Administrative Code, Article 12
Equal Employment Opportunity:
Dolby is proud to be an equal opportunity employer. Our success depends on the combined skills and talents of all our employees. We are committed to making employment decisions without regard to race, religious creed, color, age, sex, sexual orientation, gender identity, national origin, religion, marital status, family status, medical condition, disability, military service, pregnancy, childbirth and related medical conditions or any other classification protected by federal, state, and local laws and ordinances.

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