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Ml Inference Jobs in Washington, DC (NOW HIRING)

Expression

Data Platform Architect

Falls Church, VA

$68 - $87.50/hr

Design scalable ML inference approaches supporting production workloads and establish monitoring for model performance, data drift, and system health. * Support self-service ML capabilities that ...

New

Expression Networks

Data Platform Architect

Falls Church, VA · On-site

$68 - $87.50/hr

Design scalable ML inference approaches supporting production workloads and establish monitoring for model performance, data drift, and system health. * Support self-service ML capabilities that ...

New

Cynnovative

ML Engineer (LLM Systems)

Arlington, VA · On-site

$120 - $150/hr

Optimize inference performance (latency, throughput) * Batching, caching, and request scheduling * Efficient GPU/CPU utilization and memory management * Design and deploy containerized ML services (e ...

New

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 ...

Hatch IT

Data Platform Architect

Falls Church, VA · On-site

$68 - $87.50/hr

Design scalable ML inference approaches supporting production workloads and establish monitoring for model performance, data drift, and system health. * Support self-service ML capabilities that ...

New

Hatch IT

Data Platform Architect

Falls Church, VA

$68 - $87.50/hr

Design scalable ML inference approaches supporting production workloads and establish monitoring for model performance, data drift, and system health. * Support self-service ML capabilities that ...

New

Hatch IT

Data Platform Architect

Falls Church, VA · On-site

$68 - $87.50/hr

Design scalable ML inference approaches supporting production workloads and establish monitoring for model performance, data drift, and system health. * Support self-service ML capabilities that ...

New

540

AI/ML Engineer

Arlington, VA · On-site

... inference * Implement MLOps practices using CI/CD, infrastructure as code, automated testing, and ... Optimize AI/ML services and infrastructure for performance, scalability, reliability, and cost ...

540

AI/ML Engineer

Arlington, VA

... inference * Implement MLOps practices using CI/CD, infrastructure as code, automated testing, and ... Optimize AI/ML services and infrastructure for performance, scalability, reliability, and cost ...

540

AI/ML Engineer

Arlington, VA

... inference * Implement MLOps practices using CI/CD, infrastructure as code, automated testing, and ... Optimize AI/ML services and infrastructure for performance, scalability, reliability, and cost ...

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Showing results 21-40

All JobsMl Inference JobsMl Inference Jobs in Washington, DC

Ml Inference information

See Washington, DC salary details

$42.5K

$139K

$222.6K

How much do ml inference jobs pay per year?

As of Aug 21, 2026, the average yearly pay for ml inference in Washington, DC is $139,013.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,600.00 and $154,000.00 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.

What are popular job titles related to Ml Inference jobs in Washington, DC?

For Ml Inference jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Ml Inference jobs in Washington, DC look for?

The top searched job categories for Ml Inference jobs in Washington, DC are:

Ml Inference jobs near you

Data Platform Architect

Expression

Falls Church, VA

$68 - $87.50/hr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 3 days ago

New

Job description

Expression is seeking an experienced Data Platform Architect to provide architectural guidance, technical standards, and operational support for teams delivering secure, scalable data, analytics, and AI/ML solutions in mission environments.

The Data Platform Architect will work across engineering, data science, analytics, platform, and mission teams to guide implementation in Databricks and Palantir Foundry. This role will help delivery teams structure data pipelines, data products, analytics and ML workflows, and platform assets so solutions are consistent, reusable, governed, supportable, and production-ready.

The successful candidate will provide hands-on guidance spanning data integration, DataOps, DevOps, MLOps, governance, security, compliance, performance optimization, and platform operations while helping teams move solutions from prototypes into reliable production environments.

Clearance: Secret/Top Secret clearance required
Location: Falls Church, VA

Key Responsibilities

  • Provide hands-on architectural guidance to teams implementing data pipelines, analytics workflows, data products, and AI/ML capabilities in Databricks and Palantir Foundry.
  • Guide selection and implementation of platform-native capabilities for data ingestion, transformation, orchestration, model execution, analytics, and data-product delivery.
  • Advise teams on appropriate use of Databricks, Foundry, and integrated cross-platform architectures.
  • Guide the transition of prototypes and notebook-based solutions into reliable, maintainable production workflows.
  • Establish and maintain technical standards for project structure, code organization, pipeline design, workflow orchestration, testing, metadata, lineage, documentation, and platform implementation.
  • Develop reusable templates, reference architectures, and implementation patterns that improve consistency and accelerate delivery.
  • Promote scalable approaches including medallion architecture, governed data publishing, reusable transformation logic, and shared analytics and ML components.
  • Conduct technical reviews and provide actionable guidance to improve scalability, maintainability, reliability, and supportability.
  • Guide CI/CD implementation for jobs, pipelines, notebooks, packaged code, models, and data products.
  • Establish operational practices for deployment, environment promotion, monitoring, alerting, rollback, release management, observability, lineage, and data-quality validation.
  • Promote reproducible MLOps practices for model training, validation, packaging, registration, deployment, monitoring, batch inference, and lifecycle management using MLflow, Databricks workflows, and related capabilities.
  • Design scalable ML inference approaches supporting production workloads and establish monitoring for model performance, data drift, and system health.
  • Support self-service ML capabilities that enable data scientists to efficiently deploy and monitor models.
  • Define integration patterns for onboarding data sources, managing schema evolution, and connecting Databricks and Foundry with enterprise systems, applications, data warehouses, streaming platforms, APIs, and BI tools.
  • Guide implementation of secure access controls, governed data sharing, metadata management, data catalogs, lineage, traceability, and audit-ready workflows.
  • Establish data-quality standards and automated testing approaches for analytical and ML workloads.
  • Partner with stakeholders to define data definitions, business logic, governance requirements, and compliant handling of structured and unstructured data.
  • Advise teams on Spark optimization, workload design, workflow dependencies, storage and compute utilization, and other platform-performance considerations.
  • Identify and help resolve architecture, integration, reliability, and performance issues affecting production jobs, data products, and operational analytics.
  • Design data models supporting machine learning, analytics, and business intelligence requirements, including integrations with Tableau, Power BI, and Qlik Sense.
  • Build and support integrations with MAVEN Smart Systems/Palantir Foundry environments and other enterprise systems.
  • Collaborate with engineers, data scientists, BI analysts, product managers, platform and security teams, and mission stakeholders to align architecture decisions with delivery priorities.
  • Participate in design sessions, technical reviews, sprint activities, demonstrations, and cross-team problem solving.
  • Maintain technical documentation supporting implementation consistency, reuse, operational handoff, and long-term supportability.

Required Qualifications

  • 5+ years of technical experience, including 3+ years designing or implementing production solutions on Databricks, Palantir Foundry, or similar modern data platforms.
  • Strong experience with Python, SQL, PySpark, and Spark SQL for scalable data-processing workflows.
  • Experience with Palantir Foundry or comparable enterprise analytics platforms, including pipeline development, governed data delivery, lineage, and operational analytics.
  • Experience designing and operationalizing data pipelines, transformation workflows, and data products supporting structured and unstructured data.
  • Hands-on knowledge of Databricks platform capabilities such as Delta Lake, Workflows, MLflow, Unity Catalog, or similar platform-native services.
  • Familiarity with DataOps, DevOps, and MLOps practices, including CI/CD, version control, testing, deployment, monitoring, and operational support.
  • Strong understanding of data quality, metadata management, lineage, access control, and governance within secure or regulated environments.
  • Experience troubleshooting architecture, integration, performance, and operational issues across distributed data platforms.
  • Ability to establish technical standards, guide architecture and implementation decisions, and clearly communicate technical concepts to technical and non-technical stakeholders.

Preferred Qualifications

  • Deep Databricks expertise, including medallion architecture, Delta optimization, workload tuning, cluster and job strategy, and production ML enablement.
  • Experience implementing solutions in Palantir Foundry, including data-pipeline organization, governed data assets, operational workflows, and integrations.
  • Experience with Git-based CI/CD pipelines, infrastructure and deployment tooling, and cloud-native platform services.
  • Experience supporting the ML lifecycle, including model packaging, registration, deployment, monitoring, and inference-workflow integration.
  • Knowledge of enterprise data integration, API-based data exchange, and secure cross-platform interoperability.
  • Experience with Advana/MAVEN Smart System (Palantir Foundry) or similar DoD enterprise analytics environments.
  • Prior experience supporting Department of Defense, Intelligence Community, or other Federal mission environments.

Benefits:

Expression offers competitive salaries and benefits, such as:

  • 401k matching
  • PPO and HDHP medical/dental/vision insurance
  • Education reimbursement up to $10,000/yr
  • Complimentary life insurance
  • Generous PTO and 11 days of holiday leave
  • Onsite gym facility and trainer
  • Commuter Benefits Plan
  • In-office Cold Brew Coffee

About Expression:

Founded in 1997 and headquartered in Washington DC, Expression provides data fusion, data analytics, AI/ML, software engineering, information technology, and electromagnetic spectrum management solutions to the U.S. Department of Defense, Department of State, and national security community. Expression's culture focuses on creating immediate and sustainable value for our clients via agile delivery of tailored solutions built through constant engagement with our clients. Expression was ranked #1 on the Washington Technology 2018's Fast 50 list of fastest-growing small business Government contractors and a Top 20 Big Data Solutions Provider by CIO Review.

We make sure to provide everyone with the tools and opportunities to grow while working on some of the newest technologies in the industry. We get excited about celebrating our professionals' milestones, accomplishments, promotions, overcoming challenges, and many other aspects that make an engaging collaborative environment.

Equal Opportunity Employer/Veterans/Disabled
Expression is an Equal Opportunity Employer. If you require a reasonable accommodation during the application or interview process, please submit your request to our Human Resources department through the application portal.

Employment Type: FULL_TIME

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