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Ml Inference Jobs in Portland, OR (NOW HIRING)

Coulson Aviation

CV/ML Engineer

Portland, OR · On-site

$140K - $190K/yr

Work with internal Coulson IT on model deployment and inference infrastructure * Contribute to the ... Background in remote sensing, geospatial ML, or environmental monitoring * Familiarity with real ...

Central Business Solutions

AI Engineer

Hillsboro, OR · On-site

Role Summary Build intelligent capabilities using LLM-based inferencing, agentic AI workflows, and RAG-based solutions leveraging AWS-native AI/ML services. Focus on inference orchestration, vector ...

Deloitte

AI Engineer Senior Consultant

Portland, OR · Hybrid

$110K - $152K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). * Implement safety, privacy, and ...

Deloitte

AI Engineer Consultant

Portland, OR · 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

Portland, OR · Hybrid

Deliver governed data and features for ML/GenAI (curated datasets, feature pipelines/serving) supporting training and real-time inference, including consistency, caching, backfills, and latency SLOs.

Deloitte

AI Engineer III

Portland, OR · 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 ...

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Ml Inference information

See Portland, OR salary details

$39.8K

$130.2K

$208.4K

How much do ml inference jobs pay per year?

As of Jun 11, 2026, the average yearly pay for ml inference in Portland, OR is $130,165.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,500.00 and $144,200.00 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.

Which 3 jobs will survive AI?

For ML Inference roles, jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to persist, such as data scientists, AI ethics specialists, and machine learning engineers. These roles involve tasks that are difficult to automate and often require specialized skills, domain knowledge, and critical thinking. Continuous learning and expertise in AI tools and programming languages like Python or TensorFlow can also enhance job security in this field.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, specialized skills in deep learning, and strong industry demand can earn $500,000 or more annually, especially in high-cost-of-living areas or within top tech companies. Achieving this level typically requires advanced degrees, certifications, and a proven track record of impactful projects.

What is a $900,000 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 requiring advanced skills in deep learning, data science, and experience with tools like TensorFlow or PyTorch. These positions usually involve leadership responsibilities, strategic planning, and may require multiple years of specialized experience or advanced degrees.

Is ML a high paying job?

Machine Learning (ML) inference roles are generally well-paid due to the specialized skills required, such as knowledge of algorithms, programming, and data analysis. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech positions. Advanced roles often require proficiency with tools like TensorFlow or PyTorch and may include certifications or advanced degrees.

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, 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.
What are popular job titles related to Ml Inference jobs in Portland, OR? For Ml Inference jobs in Portland, OR, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Portland, OR look for? The top searched job categories for Ml Inference jobs in Portland, OR are:
What cities near Portland, OR are hiring for Ml Inference jobs? Cities near Portland, OR with the most Ml Inference job openings:
Ml Inference jobs near you

CV/ML Engineer

Coulson Aviation

Portland, OR • On-site

$140K - $190K/yr

Full-time

Posted 19 days ago

Job description

ABOUT THE PROGRAM

EmberWorks Sensing (part of the new R&D division within Coulson Aviation) is building a sensor network to deploy across a fleet of operational fire-fighting aircraft. The goal is to deliver real-time fire data and ongoing analytics to host agencies without changes to the existing missions. This is an early-stage product team working on impactful new technology for wildfire suppression efforts.

THE ROLE

This is a remote position with occasional travel. You will own all machine learning and computer vision work on the team. Your primary focus will be applying CV and ML to sensor data from operational wildfire aircraft to turn raw imagery into accurate, actionable outputs for the agencies on the ground. The work will grow in scope as the program matures and the data starts flowing.

WHAT YOU'LL OWN

  • Design and train CV/ML models to extract meaningful fire intelligence from sensor data
  • Establish model validation workflows using ground-truth perimeter data from host agencies
  • Iterate on model accuracy by fire type, terrain, and atmospheric condition
  • Shape the roadmap for how ML can extend beyond detection into predictive and operational analytics
  • Work with internal Coulson IT on model deployment and inference infrastructure
  • Contribute to the strategy for how real-time video processing can extend the system’s operational value

WHAT WE'RE LOOKING FOR

  • 3+ years applied ML or CV engineering in a production environment
  • Strong experience with image segmentation, object detection, or scene classification (PyTorch, TensorFlow, or equivalent)
  • Comfortable taking a model from training through to deployed inference
  • Experience working with noisy, real-world sensor data rather than clean benchmark datasets
  • Ability to define and track model performance metrics independently

NICE TO HAVE

  • Experience with thermal or infrared imagery — satellite, aerial, or ground-based
  • Background in remote sensing, geospatial ML, or environmental monitoring
  • Familiarity with real-time or near-real-time video inference pipelines
  • Understanding of fire behavior, wildland fire operations, or related domains
  • Experience with edge or embedded inference (onboard processing)
  • Familiarity with photogrammetry or structure-from-motion pipelines
  • Preference for west coast location. Extra points for PNW

A NOTE ON THE ROLE

This role is intentionally broad for a small team. You will be the only ML engineer for a significant period. The ideal candidate is someone who has built models for real-world problems and is energized by working close to the operational use case rather than at arm's length from it.

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