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Ml Inference Jobs in Oregon (NOW HIRING)

Automation Anywhere, Inc.

OR · On-site

Driving the development of large-scale ML infrastructure, ensuring low-latency inference and efficient resource utilization across cloud and hybrid environments * Implementing MLOps best practices ...

Nvidia

OR · Hybrid

Publish and present technical work on novel compilation approaches for inference and related spatial accelerators at top tier ML, compiler, and computer architecture venues. What we need to see: * MS ...

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 ...

Nvidia

OR · On-site

$104K - $143K/yr

... ML research and production evaluation. You'll ship systems that run at scale on real-world driving ... Develop agentic workflows that chain model inference, retrieval, and structured reasoning to ...

Nvidia

OR · On-site

$122K - $161K/yr

... with ML/DL systems development preferable * Strong experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc) and ideally inference engines and runtimes ...

Nvidia

OR

$122K - $161K/yr

... with ML/DL systems development preferable * Strong experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc) and ideally inference engines and runtimes ...

Gruve

OR · On-site

$220K - $275K/yr

Define and drive the end-to-end networking strategy for AI inference data centers, including fabric ... Proven experience architecting high-performance networks for AI/ML, HPC, or cloud infrastructure ...

Netflix

OR

$466K - $750K/yr

Data Science and Engineering ('DSE') at Netflix is aimed at using data, analytics, causal inference, machine learning (ML), and sciences to improve various aspects of our business. The AI initiative ...

Netflix

OR · On-site

$466K - $750K/yr

Data Science and Engineering ('DSE') at Netflix is aimed at using data, analytics, causal inference, machine learning (ML), and sciences to improve various aspects of our business. The AI initiative ...

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Ml Inference information

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, 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 Oregon? For Ml Inference jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Oregon look for? The top searched job categories for Ml Inference jobs in Oregon are:
What cities in Oregon are hiring for Ml Inference jobs? Cities in Oregon with the most Ml Inference job openings:
Ml Inference jobs near you
Staff Machine Learning Engineer

Staff Machine Learning Engineer

Automation Anywhere, Inc.

OR • On-site

Full-time

Medical, PTO

Posted 14 days ago

Job description

About Us:

Automation Anywhere is the leader in Agentic Process Automation (APA), transforming how work gets done with AI-powered automation. Its APA system, built on the industry's first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analytics-all delivered with enterprise-grade security and governance. Guided by its vision to fuel the future of work, Automation Anywhere helps organizations worldwide boost productivity, accelerate growth, and unleash human potential.

Our opportunity:

Automation Anywhere, the leader in Agentic Process Automation (APA), is seeking a Staff Machine Learning Engineer to help power the next generation of AI-driven digital agents transforming enterprise operations.

In this role, you will design, build, and deploy cutting-edge machine learning systems that operate at real-world scale-advancing Generative AI, Natural Language Processing, and Computer Vision capabilities within our industry-leading platform. You will partner closely with product, engineering, data science, and platform teams to translate breakthrough research into high-impact production systems used by global enterprises.

This is a highly visible technical leadership opportunity where you will architect robust ML infrastructure, champion modern MLOps practices, and optimize performance, scalability, and reliability across distributed environments. If you are passionate about turning advanced AI into enterprise-grade solutions that deliver measurable business outcomes, this is your chance to shape the future of intelligent automation at scale.

Who you'll report to:

This role reports to our Director, ML Engineering

Location:

Hybrid role with regular onsite work days in our San Jose, CA office strongly preferred. Other U.S locations may be considered.

You will make an impact by being responsible for:

  • Developing and optimizing machine learning models leveraging NLP, Computer Vision, and GenAI
  • Architecting and implementing scalable ML pipelines for training, validation, deployment, and monitoring of production models
  • Driving the development of large-scale ML infrastructure, ensuring low-latency inference and efficient resource utilization across cloud and hybrid environments
  • Implementing MLOps best practices, automating model training, validation, deployment, and performance monitoring
  • Working closely with data engineers, software engineers, and product teams to ensure seamless integration of ML solutions into production systems
  • Optimizing ML models for performance, scalability, and efficiency, leveraging techniques like quantization, pruning, and distributed training
  • Enhancing model reliability by implementing automated monitoring, CI/CD pipelines, and versioning strategies
  • Leading efforts in data acquisition and preprocessing, including annotation and refinement of datasets to improve model accuracy
  • Staying updated with state-of-the-art ML research, identifying opportunities to integrate new techniques and technologies into production systems

You will be a great fit if you have:

  • 7+ years of hands-on experience designing, building, and deploying machine learning models, with expertise in NLP, Computer Vision, and/or Generative AI solutions
  • Proven experience taking ML models from development to production, ensuring scalability, reliability, high availability, and ongoing performance monitoring
  • Strong proficiency in Python (required) and working knowledge of R and SQL, with experience leveraging big data technologies (e.g., Spark, Hadoop) for large-scale data processing and analytics
  • Deep experience with modern ML frameworks such as TensorFlow and PyTorch, including model training, evaluation, optimization (e.g., quantization, pruning), and inference performance tuning
  • Experience building and managing end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and lifecycle management
  • Hands-on experience implementing MLOps best practices, including CI/CD for ML, automated model versioning, monitoring for drift/performance, and workflow automation
  • Experience with cloud-based ML platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform) for training, deploying, and scaling models in cloud environments
  • Practical experience with containerization and orchestration tools (e.g., Docker, Kubernetes) and model serving platforms (e.g., Triton, ONNX) for production-grade deployments
  • Experience fine-tuning large language models (LLMs) and applying Generative AI techniques preferred
  • Familiarity with distributed training across multi-GPU or cloud environments preferred

You excel in these key competencies:

  • Excellent problem-solving skills, with the ability to break down complex challenges in document extraction and transform them into scalable ML solutions
  • Strong communication skills, with the ability to articulate ML problems clearly and work autonomously
  • Ability to work cross-functionally with engineering, product, and data teams, influence technical direction without formal authority, and drive alignment across stakeholders in a fast-paced environment
  • Capacity to connect technical ML solutions to broader business objectives, prioritize high-impact initiatives, and make pragmatic trade-offs that balance innovation with production reliability
  • Demonstrates curiosity and agility in staying ahead of rapidly evolving AI/ML advancements, quickly evaluating new technologies, and applying them responsibly to real-world enterprise challenges

The base salary range for this position is $155,000 - $175,000 a year. The base salary ultimately offered is determined through a review of education, industry experience, training, knowledge, skills, abilities of the applicant in alignment with market data and other factors. This position is also eligible for a discretionary bonus, equity and a full range of medical and other benefits.

Ready to Revolutionize Work? Join Us.

This is an opportunity to work with a global, passionate team pioneering technology that's redefining the way people work, everywhere. Join us and discover the many ways that you can have an impact, achieve your potential, and go be great.

Job Segment OR Key Words: SaaS, Machine Learning, ML, Engineering, NLP, Generative AI, APA, Agentic Process Automation

#LI-JS1

Benefits and perks you'll appreciate:

  • Flexible work schedule / remote roles
  • Unlimited Personal Time Off
  • 12 holidays off per year
  • 4 days volunteer time off per year
  • Eligible for 4 company Achievement days off per year
  • Variety of health care and well-being benefits
  • Paid family/parental leave
  • We are a designated "Best Place to Work" for 2 years in a row! Learn morehere
  • Newsweek's Top 100 Most Loved Workplaces in America 2023 - Learn morehere

Automation Anywhere is an Affirmative Action and Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, national origin, genetic information, age, disability, veteran status, or any other legally protected basis.

If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email recruiting@automationanywhere.com.

At this time, we typically do not offer visa sponsorship for this position. Candidates should generally be authorized to work in the United States without the need for current or future sponsorship.

All unsolicited resumes submitted to any @automationanywhere.com email address, whether submitted by an individual or by an agency, will not be eligible for an agency fee.

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