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Entry Level Machine Learning Engineer Jobs in Redmond, WA

Unitytech

Staff Machine Learning Engineer

Bellevue, WA · On-site

$244K - $366K/yr

... machine learning pipelines, and business operations. As data volume and complexity grow, our ... We are seeking a staff ML engineer to design and evolve the large-scale offline platform. This role ...

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Entry Level Machine Learning Engineer information

See Redmond, WA salary details

$33.6K

$77.7K

$132.2K

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

As of Jul 14, 2026, the average yearly pay for entry level machine learning engineer in Redmond, WA is $77,682.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,700.00 and $87,900.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Entry Level Machine Learning Engineer position, and why are they important?

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 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 is an Entry Level Machine Learning Engineer job?

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 the most commonly searched types of Machine Learning Engineer jobs in Redmond, WA? The most popular types of Machine Learning Engineer jobs in Redmond, WA are:
What are popular job titles related to Entry Level Machine Learning Engineer jobs in Redmond, WA? For Entry Level Machine Learning Engineer jobs in Redmond, WA, the most frequently searched job titles are:
What cities near Redmond, WA are hiring for Entry Level Machine Learning Engineer jobs? Cities near Redmond, WA with the most Entry Level Machine Learning Engineer job openings:
Entry Level Machine Learning Engineer jobs near you

Machine Learning Engineer - Simulation Framework

Zoox

Seattle, WA • On-site

$151K - $257K/yr

Full-time

Medical, Life, PTO

Posted 20 days ago

Job description

Simulation is essential for Zoox to rapidly iterate on our driving software and hardware, and to validate our safety before we drive in the real world. We create virtual worlds to challenge our robots, from real-world data, entirely novel scenarios, or a combination of both. Our simulations need to run at a huge scale to cover everything that might happen, and to help prove our driving to be safe.
As a Machine Learning Engineer on the Simulation Core Team, you will focus on the intersection of machine learning and synthetic environments within our high-speed, GPU-based simulation framework. Our success depends on you driving ML efficiency while solving complex "sim-to-sim" and "sim-to-real" fidelity gaps, ensuring our safety-critical models train on data that perfectly aligns with physical vehicle behavior.
In this role, you will:
  • Develop and optimize our GPU-based simulation framework to support complex machine learning training and validation pipelines.
  • Apply reinforcement learning concepts to solve complex behavioral and path planning challenges in simulation environments.
  • Identify and resolve "sim-to-sim" and "sim-to-real" fidelity gaps to ensure parity between high-speed ML simulations, high-fidelity 3D environments, and physical vehicle execution.
  • Build systems that allow autonomy users to self-serve data generation and accelerate their training iterations.
  • Write robust, production-ready code to integrate advanced ML algorithms directly into our core simulation architecture.

Qualifications:
  • PhD or Master's in computer science, robotics, machine learning, or a related field.
  • Deep understanding of reinforcement learning and its application in simulated or robotic environments.
  • Hands-on experience developing, training, and fine-tuning deep learning models using modern frameworks (e.g., JAX or PyTorch).
  • Strong proficiency in C++ and Python for building and deploying production machine learning systems.
  • Experience analyzing and bridging fidelity gaps between synthetic training data and real-world execution.

Bonus Qualifications:
  • Experience with GPU programming (CUDA) or high-performance compute clusters.
  • Automotive or autonomous robotics industry experience.
  • Strong background in deterministic systems and latency optimization.

$151,000 - $257,000 a year
Base Salary Range
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We're looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
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Accommodations
If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.
A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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