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Junior Full Stack Machine Learning Engineer Jobs in Salem, OR

Bespoke Labs

Machine Learning Engineer

Salem, OR · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

DataAnnotation

SDLC Engineer - AI Trainer

Salem, OR · Remote

$50 - $100/hr

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

DataAnnotation

QA Engineer - AI Trainer

Salem, OR · Remote

$50 - $100/hr

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

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All JobsFull Stack Machine Learning Engineer JobsJunior Full Stack Machine Learning Engineer JobsJunior Full Stack Machine Learning Engineer Jobs in Salem, OR

Junior Full Stack Machine Learning Engineer information

See Salem, OR salary details

$46.7K

$95K

$142.7K

How much do junior full stack machine learning engineer jobs pay per year?

As of Jul 14, 2026, the average yearly pay for junior full stack machine learning engineer in Salem, OR is $95,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,400.00 and $96,000.00 per year, depending on experience, location, and employer.

What is the difference between Junior Full Stack Machine Learning Engineer vs Junior Data Scientist?

AspectJunior Full Stack Machine Learning EngineerJunior Data Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or Master's in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentDevelops end-to-end ML applications, works on both backend and frontendAnalyzes data, builds models, and visualizes insights, mainly in data analysis tools
Employer & Industry UsageTech companies, startups, AI-focused firmsResearch institutions, tech companies, finance, healthcare

While both roles involve working with data and machine learning, the Junior Full Stack Machine Learning Engineer focuses on building complete applications with ML components, including frontend and backend development. The Junior Data Scientist primarily analyzes data, creates models, and provides insights without necessarily developing full applications.

What are popular job titles related to Junior Full Stack Machine Learning Engineer jobs in Salem, OR? For Junior Full Stack Machine Learning Engineer jobs in Salem, OR, the most frequently searched job titles are:
What job categories do people searching Junior Full Stack Machine Learning Engineer jobs in Salem, OR look for? The top searched job categories for Junior Full Stack Machine Learning Engineer jobs in Salem, OR are:
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Junior Full Stack Machine Learning Engineer jobs near you

Machine Learning Engineer

Bespoke Labs

Salem, OR • On-site

Full-time

Posted 26 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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