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Ml Inference Jobs in Georgia (NOW HIRING)

Equifax, Inc.

Senior Applied AI Engineer

Alpharetta, GA · On-site

$100K - $138K/yr

Lead the end-to-end design of AI systems including LLM-powered applications, NLP pipelines, and ML inference infrastructure at enterprise scale. * Evaluate and recommend AI frameworks, cloud services ...

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 ...

Dolby Laboratories, Inc.

AI Architect

Atlanta, GA

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 ...

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 ...

Honeywell

Sr. Director Data & AI Platforms

Atlanta, GA · On-site

$64.75 - $86.50/hr

Design cloud-native AI platform architectures on major hyperscalers including managed AI/ML services, serverless inference, cloud-native data platforms, and AI gateway patterns. * Architect for edge ...

Honeywell

Sr. Director Data & AI Platforms

Atlanta, GA · On-site

$64.75 - $86.50/hr

Design cloud-native AI platform architectures on major hyperscalers including managed AI/ML services, serverless inference, cloud-native data platforms, and AI gateway patterns. * Architect for edge ...

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 ...

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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Ml Inference information

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.

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 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.

What engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.

Which 3 jobs will survive AI?

Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.

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.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

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.
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Ml Inference jobs near you
Software Engineer (SWE/SWE II), AI Platform- Slack

Software Engineer (SWE/SWE II), AI Platform- Slack

Salesforce

Atlanta, GA • On-site

$93K - $128K/yr

Full-time

Medical, Dental, Vision, Life, Retirement

Posted 11 days ago

Salesforce rating

8.0

Company rating: 8.0 out of 10

Based on 57 frontline employees who took The Breakroom Quiz

96th of 202 rated software companies

Job description

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category

Software Engineering

Job Details

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword - it's a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You're in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

The Experience
Slack AI's mission is to transform how people work by making Slack an AI-powered operating system. We're tackling significant challenges like unlocking collective knowledge and reducing noise, all while building a seamless, consumer-grade AI experience within users' existing workflows. Join us in shaping the future of work through AI.
The software engineer role at Salesforce encompasses architecture, design, implementation, and testing to ensure we build products right and release them with high quality. Equally important is advanced prompt engineering - the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.
The AI and ML Infrastructure team is part of Slack's Core Infrastructure organization and is responsible for the foundational systems that enable machine learning and AI across the company. The team designs, builds, and operates reliable, scalable, and high performance platforms that allow product and ML teams to develop, deploy, and operate AI driven capabilities with confidence.
The team owns shared infrastructure, services, and tooling that support the full ML lifecycle, including model training, deployment, inference, and monitoring. As Slack AI continues to grow, the team is evolving from traditional ML deployments toward large scale, highly distributed model systems. This work involves deep architectural decisions around scalable model deployment strategies, real time feature serving at very high throughput, GPU accelerated inference at message scale, and responsible training of models on sensitive data with strong privacy and safety requirements.
Core Focus Areas:
  • ML Infrastructure - The ML Infrastructure focus area is responsible for the low level systems that power training and inference at scale. This includes architecting and maintaining distributed systems for model training, serving, and deployment using Kubernetes based platforms, GPU infrastructure, and open source ML stacks such as KubeRay and vLLM. The team delivers platform capabilities that improve the speed, reliability, and quality of ML development, including training pipelines, feature generation systems, and compute orchestration.
  • AI Platform - The AI Platform focus area builds the tooling and platform layers that enable AI development across Slack. This includes creating developer facing tools, SDKs, and workflows that allow product teams to integrate AI into Slack features efficiently and safely. The platform supports LLM efficiency and model transition initiatives through integrations with managed services across multiple cloud providers acting as the connective layer between core infrastructure and product engineering teams.

We are looking for Software Engineers to join the AI Platform effort and build the developer experience that powers AI at Slack. In this role, you will treat internal engineers as your primary customers, designing and building tooling, SDKs, and evaluation frameworks that enable product teams to ship AI features faster and more reliably.
You will work closely with ML Infrastructure, modeling, and product teams to make informed decisions around open source versus managed solutions, improve the usability and reliability of our AI platforms, and accelerate the adoption of AI across Slack.
What You'll Actually Be Doing
  • Drive the evolution of Slack's AI and ML platform toward a self service, developer friendly environment that improves velocity and reliability
  • Build and maintain SDKs, feature generation tools, and CI CD pipelines that make it easy for product teams to integrate AI into their workflows
  • Manage and evolve integrations with managed AI services across multiple cloud providers
  • Design and operate AI quality evaluation frameworks and prompt engineering infrastructure to ensure AI features meet high standards for reliability and user experience
  • Collaborate closely with ML modeling, AI quality, and product engineering teams to design platforms that meet evolving technical and business needs
  • Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
  • Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
  • Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
  • Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance

You're Our Person If...
  • Experience building developer tooling, libraries, or CI CD pipelines that improve engineering speed, quality, and usability
  • Experience operationalizing Large Language Models (LLMs) and building integrations with first party APIs and external cloud provider APIs such as AWS, GCP, or Azure
  • Experience with AI quality evaluation frameworks, prompt engineering infrastructure, or developer tooling for ML workflows
  • Strong proficiency in Python, PHP or Hacklang and experience with infrastructure as code and modern software engineering practices
  • Ability to communicate complex technical concepts clearly and effectively to a broad range of stakeholders
  • Love to model modern methodologies for unit tests, code review, design documentation, debugging, and troubleshooting.
  • Are curious, inquisitive, and determined to fix things when they break.
  • Work well with a team of diverse backgrounds and experience on complicated projects.
  • A related technical degree required
  • A demonstrated, genuine AI-first approach to engineering. Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.
  • Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows
  • Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.

Unleash Your Potential

When you join Salesforce, you'll be limitless in all areas of your life. Our benefits and resources support you to find balance andbe your best, and our AI agents accelerate your impact so you cando your best. Together, we'll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future - but to redefine what's possible - for yourself, for AI, and the world.

Accommodations

If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form.

Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates' resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.

Posting Statement

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that's inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications - without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.

In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. The typical base salary range for this position is $117,200 - $223,900 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.

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