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Python Ml Developer Jobs in Everett, WA (NOW HIRING)

lululemon

Applied AI/ML Scientist

Seattle, WA

You own the AI solution implementation and partner with engineering teams to integrate solutions ... such as Python, pyTorch, cloud ML platforms and libraries, open-source toolkits. Must haves ...

Lululemon

Applied AI/ML Scientist

Seattle, WA · On-site

Collaborating with product and engineering teams to embed AI models into end to end business ... Experience with ML implementation using commonly used tools such as Python, pyTorch, cloud ML ...

Expedia

Senior AI / ML Data Engineer - Layla AI

Seattle, WA · On-site

$120K - $163K/yr

Proficiency with Python for data engineering work. * Demonstrated ability to own data quality, reliability, and observability for critical datasets in production. * Familiarity with supporting AI/ML ...

Apple Inc.

ML Engineer - Creator Studio

Seattle, WA · On-site

Strong programming skills in Python and experience with deep-learning toolkits like PyTorch, JAX ... as Core ML. * Experience with Swift and iOS/macOS development. * A record of publications or ...

The AES Group

Senior DevOps Engineer

Seattle, WA · Remote

$133K - $170K/yr

... Python automation, and Azure cloud technologies, including Azure Databricks, to build scalable ... Work on cutting-edge cloud transformation and AI/ML infrastructure projects * Opportunity to ...

ARM

Senior AI Engineer

Seattle, WA · Hybrid

$170K - $231K/yr

As a Senior Machine Learning DevOps Engineer, you will design and build the ML DevOps ... Robust programming and scripting skills preferably in Python / C / C++, previous experience with ...

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All JobsPython Ml Developer JobsPython Ml Developer Jobs in Everett, WA

Python Ml Developer information

See Everett, WA salary details

$14

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How much do python ml developer jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for python ml developer in Everett, WA is $64.76, according to ZipRecruiter salary data. Most workers in this role earn between $53.37 and $73.56 per hour, depending on experience, location, and employer.

What does a Python ML Developer do?

A Python ML Developer designs, builds, and deploys machine learning models using the Python programming language. They work with large datasets, clean and process data, select appropriate algorithms, and use libraries like TensorFlow, PyTorch, or scikit-learn to implement solutions. Their work often involves collaborating with data scientists and engineers to integrate machine learning models into applications. Additionally, they may be responsible for testing, tuning, and optimizing models to achieve the best possible performance in real-world scenarios.

What are the key skills and qualifications needed to thrive as a Python ML Developer?

To thrive as a Python ML Developer, you need strong programming skills in Python, a solid understanding of machine learning algorithms, and a background in mathematics or statistics, often supported by a degree in computer science, engineering, or a related field. Familiarity with tools and libraries such as TensorFlow, scikit-learn, PyTorch, and version control systems like Git is essential, along with experience using data visualization and cloud platforms. Critical soft skills include problem-solving, adaptability, and effective communication to collaborate with cross-functional teams and explain complex models to stakeholders. These skills ensure the successful development, deployment, and maintenance of machine learning solutions that drive business value.

What are some common challenges Python ML Developers face when deploying machine learning models to production?

Python ML Developers often encounter challenges such as ensuring model scalability, managing dependencies, and maintaining reproducibility when deploying models into production environments. Integrating machine learning models with existing systems can require close collaboration with DevOps and software engineering teams to streamline workflows and automate deployment pipelines. Additionally, monitoring model performance over time and handling data drift are crucial responsibilities to ensure continued accuracy and reliability of deployed solutions.

What is the difference between Python Ml Developer vs Data Scientist?

AspectPython Ml DeveloperData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; Python, ML certificationsBachelor's/Master's in Data Science, Statistics, or related; Python, ML certifications
Work EnvironmentSoftware development teams, AI/ML projectsResearch, data analysis, modeling teams
Employer & Industry UsageTech companies, startups, AI firmsFinance, healthcare, tech, research institutions
Common Search & ComparisonYesYes

Python ML Developers focus on building and deploying machine learning models using Python, often working closely with software engineering teams. Data Scientists analyze data, create models, and generate insights, often using Python along with statistical tools. While both roles require Python and ML knowledge, Python ML Developers are more involved in implementation and deployment, whereas Data Scientists focus on data analysis and research.

What job categories do people searching Python Ml Developer jobs in Everett, WA look for?

The top searched job categories for Python Ml Developer jobs in Everett, WA are:

What cities near Everett, WA are hiring for Python Ml Developer jobs?

Cities near Everett, WA with the most Python Ml Developer job openings:

Python Ml Developer jobs near you
Infographic showing various Python Ml Developer job openings in Everett, WA as of September 2026, with employment types broken down into 2% Internship, 83% Full Time, 10% Part Time, and 5% Contract. Highlights an 75% Physical, 5% Hybrid, and 20% Remote job distribution, with an average salary of $134,696 per year, or $64.8 per hour.

Staff Software Engineer, AI/ML

Seattle, WA • Hybrid

DigitalOcean
Software Development • 501 - 1,000 employees
Learn more about DigitalOceanexternal

Full-time

Re-posted 19 days ago

Key responsibilities

  • Own the feedback learning roadmap by defining and executing the applied research agenda for feedback-driven agentic AI, including reward modeling and preference optimization.

  • Design and implement learning loops and evaluation frameworks to improve agent reasoning, planning, and action execution, and measure their effectiveness at scale.

  • Set technical direction across modeling, experimentation, and evaluation, and collaborate with cross-functional teams to move research prototypes into production.

Job description

Building AI agents that take real actions is the easy part. Building agents that get better over time - that learn from feedback, correct mistakes, and optimize toward outcomes users actually care about - is one of the hardest open problems in production AI today.

That's what this team works on. As a Staff AI/ML Engineer on our Applied Research team, you'll own the technical direction for feedback-driven learning in DigitalOcean's agentic systems: reward modeling, preference optimization, reinforcement learning, and the evaluation infrastructure needed to measure whether any of it is actually working.

This is a senior IC role with broad technical scope. You'll set direction, run experiments at scale, and close the loop between user signals and model behavior - shipping research into production, not just writing it up.

What You'll Be Doing

Own the feedback learning roadmap

  • Define and execute the applied research agenda for feedback-driven agentic AI - from reward modeling and preference optimization to online learning and human feedback loops.
  • Translate user feedback, human evaluation data, and product signals into concrete training and optimization strategies.
  • Stay close to the research frontier on RLHF, RLAIF, DPO, PPO, GRPO, and related methods and know when to apply them versus when simpler approaches win.

Build production learning systems

  • Design and implement learning loops that improve agent reasoning, planning, tool use, and action execution over time.
  • Build evaluation frameworks that measure what matters: reasoning quality, instruction following, task success, safety, and real user outcomes - at both offline and online scale.
  • Run large-scale experiments that connect model changes to measurable improvements in user experience and business impact.

Provide technical leadership

  • Set technical direction across modeling, experimentation strategy, evaluation design, and production readiness - without requiring direct management authority.
  • Partner closely with product, engineering, design, and research teams to move work from prototype to shipped capability.
  • Communicate complex AI systems clearly to both technical and non-technical stakeholders.
What You'll Add to DigitalOcean

We're looking for engineers who have shipped real learning systems - not just prototyped them. You likely bring:

  • 8+ years of experience building production AI/ML systems - LLMs, GenAI, agentic systems, recommendation, search, personalization, or applied research at scale.
  • Hands-on experience improving AI systems through reinforcement learning, reward modeling, fine-tuning, human feedback, or preference optimization - with results you can point to.
  • Strong understanding of agentic AI: reasoning, planning, tool use, action execution, instruction following, and self-correction.
  • Strong software engineering in Python and at least one production systems language.
  • The judgment to balance model quality, product impact, latency, reliability, cost, and maintainability - and communicate those tradeoffs clearly.
Preferred Qualifications

Strong signal

  • Experience with agent evaluation, offline/online experiments, and human feedback loops in production.
  • Direct experience with RLHF, RLAIF, DPO, PPO, GRPO, or related optimization techniques.
  • Prior Staff, Senior Staff, Tech Lead, or equivalent senior IC experience.

Nice to have

  • Master's or PhD in CS, ML, AI, or a related field - or equivalent depth demonstrated through industry work.
  • Experience with production ML infrastructure: model serving, observability, data pipelines, feature stores, or experimentation platforms.
  • Research contributions via publications, patents, open-source work, or demonstrated applied research impact in RL, reward modeling, evaluation, or recommendation systems.
Compensation Range: 
  • $271,000 - $216,800

*This is a hybrid role

JR: 2026-7947

#LI-Hybrid

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