ZipRecruiter

Log In

1

Ml Inference Jobs in Seattle, WA (NOW HIRING)

Weyerhauser Co

ML Engineer

Seattle, WA · On-site

Model Deployment & Serving Operationalize and deploy batch and real-time inference solutions using ... ML System Design & Integration Design end-to-end ML systems that integrate seamlessly with ...

CoreWeave

Technical Program Manager, Inference

Bellevue, WA · On-site

$144K - $187K/yr

... ML platform engineering • Proven experience driving large-scale infrastructure or platform ... distributed inference systems, GPU compute, cloud-native architectures, and performance ...

Weyerhaeuser Company

ML Engineer

Seattle, WA

Model Deployment & Serving Operationalize and deploy batch and real-time inference solutions using ... ML System Design & Integration Design end-to-end ML systems that integrate seamlessly with ...

Weyerhaeuser Company

ML Engineer

Seattle, WA

Model Deployment & Serving Operationalize and deploy batch and real-time inference solutions using ... ML System Design & Integration Design end-to-end ML systems that integrate seamlessly with ...

CoreWeave

Senior Software Engineer, Inference

Bellevue, WA · On-site

$137K - $181K/yr

The Senior Software Engineer, Inference will lead designs, improve engineering standards, and ... to-end ML system performance by developing and tuning CUDA kernels, reducing model latency ...

previous page

Showing results 41-60

All JobsMl Inference JobsMl Inference Jobs in Seattle, WA

Ml Inference information

See Seattle, WA salary details

$42.7K

$139.7K

$223.6K

How much do ml inference jobs pay per year?

As of Aug 19, 2026, the average yearly pay for ml inference in Seattle, WA is $139,680.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,100.00 and $154,800.00 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 Seattle, WA?

For Ml Inference jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Ml Inference jobs in Seattle, WA look for?

The top searched job categories for Ml Inference jobs in Seattle, WA are:

What cities near Seattle, WA are hiring for Ml Inference jobs?

Cities near Seattle, WA with the most Ml Inference job openings:

Ml Inference jobs near you
Infographic showing various Ml Inference job openings in Seattle, WA as of August 2026, with employment types broken down into 1% Internship, 91% Full Time, 4% Part Time, and 4% Contract. Highlights an 81% Physical, 5% Hybrid, and 14% Remote job distribution, with an average salary of $139,680 per year, or $67.2 per hour.

Senior Director, Inference Products and Optimizations

DigitalOcean

Seattle, WA • On-site

$274K - $343K/yr

Full-time

Re-posted 4 days ago

Job description

Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you'll find your place here. We value winning together-while learning, having fun, and making a profound difference for the dreamers and builders in the world.
Our Inference Engine organization is seeking an experienced Senior Director of Engineering to lead a high-performing engineering team building and scaling our Large Language Model (LLM) inference products across the control plane, model optimization, and model architecture layers. This organization sits at the heart of DigitalOcean's mission to bring our signature simplicity to optimized LLM inference.
In this role, you will own DigitalOcean's inference product suite - Serverless Inference, Dedicated Inference, Inference Router, Batch Inference, and Multimodal Inference - along with the model optimization and architecture stack that underpins them. You'll be responsible for delivering robust, cost-efficient systems that serve millions of users globally at scale and high performance.
What You'll Do:
  • Team Leadership & Development: Recruit, mentor, and coach engineers on the team, fostering a culture of ownership, technical excellence, and continuous improvement.
  • Build Performant and Scalable Inference Products: Work with Product teams to define and execute on the Product roadmap for all of DigitalOcean's Inference Products - including Serverless Inference, Dedicated Inference, Inference Router, Batch Inference and Multimodal Inference
  • Inference Optimizations and Model Architecture: Lead the design and evolution of our inference serving stack, driving deep technical strategy across vLLM, SGLang, and LLM-D to optimize throughput, latency, and GPU utilization at scale. Architect the model-serving and optimization layer - spanning quantization, KV-cache management, speculative decoding, and disaggregated serving - to deliver best-in-class performance-per-dollar across our LLM inference products.
  • Cross-Functional Partnership: Collaborate with Product Management, other engineering teams, and key stakeholders to align priorities, manage dependencies, and communicate progress and risks.
  • Operational Health: Ensure the production health, stability, and on-call rotation of all to maintain the customer SLAs.
  • Champion Best Practices: Institutionalize benchmarking frameworks, observability, and auto-tuning capabilities to guide system and infrastructure tuning efforts. Encourage contributions to open-source inference engines to advance our capabilities.
Indicators of a Good Fit:
  • Experience: 10+ years of software engineering experience, with 6+ years in a technical leadership or management role, ideally within Inference Systems or AI/ML systems.
  • Technical Depth: Deep expertise in distributed systems design, modern AI/ML technologies, Kubernetes at scale, and LLM inference, and AI workload orchestration, scheduling, and resource management. Ability to engage in deep technical discussions with your team regarding highly scalable control plane design, inference engines (vLLM, SGLang), and model architectures. Strong understanding of cloud-native multi region architectures, microservices, and distributed systems fundamentals.
  • Hardware-Aware Optimization: Strategic knowledge of GPU architectures (NVIDIA and/or AMD), interconnects (like NVLink), and hardware topology and their direct impact on AI training and inference performance.
  • Systems Engineering & Security: Familiarity with concepts in container runtime internals, system isolation, and security contexts to manage risk in shared infrastructure.
  • Observability and SLOs: Expertise in defining, tracking, and operationalizing deep infrastructure and inference metrics (e.g., TTFT, TPOT) to drive performance improvements and meet service level objectives.
  • Product Mindset: Demonstrated ability to translate complex technical requirements into user-focused product features. Understanding of the balance between innovation and reliability.
  • Communication: Excellent communication skills, with the ability to explain technical decisions to non-technical stakeholders and align diverse teams around a shared vision.
  • Ownership: A strong sense of ownership and a proactive drive to identify and resolve issues preventing your team from delivering value.
Compensation Range:
  • $274,400 - $343,000

*This is a Hybrid role
JR: 2026-8090
#LI-Hybrid
Why You'll Like Working for DigitalOcean
  • We innovate with purpose. You'll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.
  • We prioritize career development. At DO, you'll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.
  • We care about your well-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.
  • We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.
  • DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.

Apply