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Edge Ai Machine Learning Jobs in Portland, OR (NOW HIRING)

Bespoke Labs

Machine Learning Engineer

Gresham, OR · On-site

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

Bespoke Labs

Machine Learning Engineer

Portland, OR · On-site

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

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Edge Ai Machine Learning information

See Portland, OR salary details

$27K

$45.2K

$93.3K

How much do edge ai machine learning jobs pay per year?

As of Aug 8, 2026, the average yearly pay for edge ai machine learning in Portland, OR is $45,160.00, according to ZipRecruiter salary data. Most workers in this role earn between $34,500.00 and $48,800.00 per year, depending on experience, location, and employer.

What is an Edge AI Machine Learning?

An Edge AI Machine Learning job involves developing and deploying machine learning models directly on edge devices, such as IoT sensors, mobile devices, and embedded systems. This role requires expertise in optimizing AI models for low-power, low-latency environments while ensuring real-time processing. Professionals in this field work with frameworks like TensorFlow Lite, ONNX, and OpenVINO to implement AI solutions efficiently. They must also handle challenges like model compression, hardware acceleration, and data privacy.

What are some typical challenges faced in an Edge AI Machine Learning role, and how can I prepare for them?

One of the most common challenges in Edge AI Machine Learning is optimizing models to run efficiently on hardware with limited resources, while maintaining acceptable accuracy and speed. You may encounter constraints related to memory, processing power, and connectivity, which require creative engineering and a deep understanding of both machine learning and embedded systems. Collaborating closely with hardware engineers, data scientists, and software developers is typical, as solutions often span multiple technical disciplines. To prepare, staying current with advancements in model compression, quantization, and edge deployment technologies will help you tackle these challenges with confidence.

What are the key skills and qualifications needed to thrive in the Edge AI Machine Learning position?

To thrive as an Edge AI Machine Learning professional, you need a strong background in machine learning algorithms, embedded systems, and proficiency with programming languages such as Python or C++. Familiarity with edge computing platforms (like NVIDIA Jetson, Google Coral), frameworks (TensorFlow Lite, ONNX), and certifications in AI or ML can greatly enhance your qualifications. Strong problem-solving abilities, collaboration, and effective communication skills are important for adapting solutions to diverse environments and working cross-functionally. These abilities enable the successful deployment of efficient and robust AI models directly on devices, meeting the unique challenges of real-time, resource-constrained settings.

How to become an edge AI machine learning engineer?

To become an edge AI machine learning engineer, develop strong skills in machine learning, embedded systems, and programming languages like Python and C++. Gain experience with hardware platforms such as NVIDIA Jetson or Raspberry Pi, and learn to optimize models for low-power, resource-constrained environments. Earning certifications in AI, embedded systems, or IoT can also enhance your qualifications.
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Edge Ai Machine Learning jobs near you
Infographic showing various Edge Ai Machine Learning job openings in Portland, OR as of August 2026, with employment types broken down into 5% Internship, 80% Full Time, and 15% Contract. Highlights an 90% In-person, 5% Hybrid, and 5% Remote job distribution, with an average salary of $45,160 per year, or $21.7 per hour.

Machine Learning Engineer

Bespoke Labs

Gresham, OR • On-site

Full-time

Re-posted 22 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

Apply