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Ml Inference Jobs in Fort Washington, MD (NOW HIRING)

Dev Technology

iOS Mobile Developer (Swift)

Reston, VA

$100K - $160K/yr

Integration of on-device ML inference frameworks (Core ML, ONNX Runtime, WhisperKit) - loading, lifecycle, error recovery - not training the models * Real-time audio pipeline: AVAudioEngine, voice ...

Tech Tammina LLC

AI/ML Engineer

Reston, VA · On-site

AI/ML Engineer Location: Reston VA Core Responsibilities (AI/ML, Python, AWS, GenAI) Design and ... Build robust MLOps workflows, including model versioning, containerized training/inference ...

Interon IT Solutions

AI/ML Engineer

Reston, VA · On-site

AI/ML Engineer Location: Reston VA - In person interviews so need Local In EAST coast only​ Core ... Build robust MLOps workflows, including model versioning, containerized training/inference ...

Interon IT Solutions

AI/ML Engineer

Reston, VA · On-site

AI/ML Engineer (Python, AWS, GenAI) Location: Reston, VA (In-person interviews required) Candidate ... Build and support MLOps workflows: model versioning, containerized training/inference, automated ...

1 point system

AI/ML Engineer

Reston, VA · On-site

Core Responsibilities (AI/ML, Python, AWS, GenAI) * Design and implement end-to-end AI/ML and ... Build robust MLOps workflows, including model versioning, containerized training/inference ...

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All JobsMl Inference JobsMl Inference Jobs in Fort Washington, MD

Ml Inference information

See Fort Washington, MD salary details

$38.6K

$126.4K

$202.4K

How much do ml inference jobs pay per year?

As of Aug 15, 2026, the average yearly pay for ml inference in Fort Washington, MD is $126,427.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $140,100.00 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 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 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 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.

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 cities near Fort Washington, MD are hiring for Ml Inference jobs?

Cities near Fort Washington, MD with the most Ml Inference job openings:

Ml Inference jobs near you

AI/ML Platform Engineer

Surge InfoTech LLC

Alexandria, VA • On-site

Full-time

Re-posted 16 days ago

Job description

  • /No C2C option/

    • We are seeking a hands-on Senior AI/ML Platform Engineer with 10+ years of IT experience and a strong track record of building, deploying, and operationalizing AI/ML systems. The ideal candidate is a doer who excels in implementing scalable, production-grade AI/ML solutions across cloud environments.

    Core Requirements
  • 10+ years of IT/engineering experience
  • 3+ years of handson AI/ML development experience
  • 4+ years working directly with AWS services (Lambda, EC2, S3, DynamoDB, IoT Core, API Gateway, Fargate/ECS)
  • Proven experience deploying ML systems into production environments
  • Strong coding skills and ability to build systems endtoend
    • Key Skills
  • Deep Learning frameworks: TensorFlow, PyTorch, Keras
  • LLMs, prompt engineering, NLP pipelines
  • Python and Java as primary languages; strong engineering fundamentals
  • FastAPI and microservices for ML inference
  • InfrastructureasCode (Terraform)
  • Kubernetes and Docker for scalable ML workloads
  • Distributed/cloud systems design with AWS
  • Edgetocloud system integration experience
  • Handson build experience (not just design/architecture)
    • Responsibilities
  • Build and deploy productiongrade ML/AI pipelines and services
  • Develop LLMpowered and NLPdriven applications
  • Write, optimize, and maintain highquality Pythonbased ML code
  • Implement scalable infrastructure using Terraform, AWS, and Kubernetes
  • Build FastAPIbased inference services and cloud APIs
  • Collaborate with crossfunctional engineering teams to deliver highimpact systems
  • Troubleshoot, optimize, and own systems endtoend as a handson engineer
    • Preferred Skills
  • Experience with distributed systems and microservices
  • Strong understanding of ML model lifecycle, deployment patterns, and operational monitoring

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