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Entry Level Machine Learning Engineer Jobs in Seattle, WA

Bespoke Labs

Machine Learning Engineer

Tacoma, WA

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Seattle, WA · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Seattle, WA

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Chewy

Machine Learning Engineer III

Bellevue, WA · On-site +1

$148K - $225K/yr

The Chewy Sponsored Ads team is looking for a Senior Machine Learning Engineer in Bellevue, WA to help launch various innovative ads offerings for our sponsored advertisement initiatives. As ...

Bespoke Labs

Machine Learning Engineer

Tacoma, WA · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

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All JobsMachine Learning Engineer JobsEntry Level Machine Learning Engineer JobsEntry Level Machine Learning Engineer Jobs in Seattle, WA

Entry Level Machine Learning Engineer information

See Seattle, WA salary details

$34.1K

$78.9K

$134.3K

How much do entry level machine learning engineer jobs pay per year?

As of Sep 11, 2026, the average yearly pay for entry level machine learning engineer in Seattle, WA is $78,935.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,600.00 and $89,300.00 per year, depending on experience, location, and employer.

What is an entry level machine learning engineer?

An Entry Level Machine Learning Engineer is responsible for developing, testing, and deploying machine learning models under the guidance of senior engineers. They work with datasets, implement algorithms, and optimize model performance. Their role often involves data preprocessing, feature engineering, and collaborating with data scientists and software engineers. Strong programming skills in Python, knowledge of ML frameworks like TensorFlow or PyTorch, and an understanding of statistics and algorithms are essential. This position serves as a foundation for building expertise in artificial intelligence and data-driven decision-making.

What are some typical projects or tasks an entry level machine learning engineer might work on?

As an Entry Level Machine Learning Engineer, you’ll often work on tasks such as data preprocessing, feature engineering, and assisting in training and evaluating models under the guidance of senior engineers or data scientists. You may help develop prototypes, automate data collection pipelines, and collaborate with software engineers to integrate machine learning solutions into products. Working in this role typically involves frequent collaboration in a team environment, participating in code reviews, and learning best practices for scalable model deployment. These foundational experiences are designed to build your technical expertise and set the stage for future growth within the field.

What are the key skills and qualifications needed to thrive as an entry level machine learning engineer?

To thrive as an Entry Level Machine Learning Engineer, you need a solid understanding of machine learning algorithms, programming languages like Python, and a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is highly valuable, and completing online courses or certifications can further demonstrate your skills. Strong analytical thinking, attention to detail, and effective communication are important soft skills in this role. These abilities are essential because they enable you to build accurate models, work collaboratively with teams, and communicate insights to stakeholders.

What are the most commonly searched types of Machine Learning Engineer jobs in Seattle, WA?

The most popular types of Machine Learning Engineer jobs in Seattle, WA are:

What are popular job titles related to Entry Level Machine Learning Engineer jobs in Seattle, WA?

For Entry Level Machine Learning Engineer jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Entry Level Machine Learning Engineer jobs in Seattle, WA look for?

The top searched job categories for Entry Level Machine Learning Engineer jobs in Seattle, WA are:

What cities near Seattle, WA are hiring for Entry Level Machine Learning Engineer jobs?

Cities near Seattle, WA with the most Entry Level Machine Learning Engineer job openings:

Entry Level Machine Learning Engineer jobs near you
Infographic showing various Entry Level Machine Learning Engineer job openings in Seattle, WA as of September 2026, with employment types broken down into 93% Full Time, and 7% Contract. Highlights an 93% In-person, and 7% Remote job distribution, with an average salary of $78,935 per year, or $37.9 per hour.

Machine Learning Engineer

Tacoma, WA

Bespoke Labs
See more jobs at Bespoke Labsexternal

Full-time

Re-posted 26 days ago

Job description

  • Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

  • Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems

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