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Machine Learning Jobs in Dallas, OR (NOW HIRING)

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 ...

Bespoke Labs

Machine Learning Engineer

Salem, OR

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 ...

Agility Robotics

Staff AI Engineer, Perception

Salem, OR · On-site +1

$207K - $323K/yr

The Perception team is looking for a staff machine learning engineer to own the design and development of object detection and tracking algorithms. Responsibilities: * As a technical lead, you will ...

Agility Robotics

Staff AI Engineer, Perception

Salem, OR · On-site

$207K - $323K/yr

The Perception team is looking for a staff machine learning engineer to own the design and development of object detection and tracking algorithms. Responsibilities: * As a technical lead, you will ...

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All JobsMachine Learning JobsMachine Learning Jobs in Dallas, OR

Machine Learning information

See Dallas, OR salary details

$25.5K

$42.7K

$88.2K

How much do machine learning jobs pay per year?

As of Aug 4, 2026, the average yearly pay for machine learning in Dallas, OR is $42,660.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,600.00 and $46,100.00 per year, depending on experience, location, and employer.

What is a machine learning?

A Machine Learning job involves developing algorithms and models that enable computers to learn from data and make predictions or decisions without explicit programming. Professionals in this field work with large datasets, design and train machine learning models, and optimize them for performance and accuracy. Roles often require knowledge of programming languages like Python or R, experience with frameworks like TensorFlow or PyTorch, and an understanding of statistics and data science principles. Machine learning engineers and data scientists collaborate with software developers and domain experts to build AI-driven solutions for various industries.

What are the typical day-to-day responsibilities in a machine learning role?

As a machine learning professional, your daily tasks may include data preprocessing, developing and training models, evaluating performance metrics, and experimenting with algorithms to optimize results. You’ll often collaborate closely with data scientists, software engineers, and business stakeholders to align technical solutions with organizational goals. Regular activities can also involve deploying models to production, monitoring performance, and troubleshooting any issues that arise post-deployment. Staying up to date with recent ML research and participating in team discussions or code reviews are also common parts of the job.

What jobs can I get with machine learning?

With a background in machine learning, you can pursue roles such as machine learning engineer, data scientist, AI researcher, or data analyst. These positions typically require skills in programming languages like Python or R, knowledge of algorithms, and experience with tools like TensorFlow or PyTorch.

What are the key skills and qualifications needed to thrive in a machine learning position?

To thrive in Machine Learning, you need a solid background in mathematics, statistics, programming (especially Python or R), and a formal degree in computer science, data science, or a related field. Experience with popular ML frameworks (such as TensorFlow, PyTorch, or Scikit-learn), version control, and relevant certifications like AWS Certified Machine Learning are highly valued. Strong problem-solving skills, curiosity, clear communication, and the ability to work both independently and within multidisciplinary teams make candidates stand out. These skills and qualities are essential for developing robust models, staying updated with technology advancements, and collaborating effectively on complex projects.

What are popular job titles related to Machine Learning jobs in Dallas, OR? For Machine Learning jobs in Dallas, OR, the most frequently searched job titles are:
What cities near Dallas, OR are hiring for Machine Learning jobs? Cities near Dallas, OR with the most Machine Learning job openings:
Machine Learning jobs near you
Infographic showing various Machine Learning job openings in Dallas, OR as of July 2026, with employment types broken down into 1% As Needed, 70% Full Time, 27% Part Time, 1% Temporary, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $42,660 per year, or $20.5 per hour.

Machine Learning Engineer

Bespoke Labs

Salem, OR • On-site

Full-time

Re-posted 18 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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