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Quantitative Researcher Machine Learning Jobs in Houston, TX

Bespoke Labs

Machine Learning Engineer

Houston, TX · 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 ...

NobleAI

Lead Machine Learning Engineer

Houston, TX · On-site +1

$97K - $128K/yr

... and researchers to create better solutions faster. At NobleAI, we are developing the next ... MSc (preferred) or BSc in Computer Science, Artificial Intelligence, or a related quantitative ...

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$50.1K

$113.8K

$187.7K

How much do quantitative researcher machine learning jobs pay per year?

As of Aug 10, 2026, the average yearly pay for quantitative researcher machine learning in Houston, TX is $113,800.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,000.00 and $145,600.00 per year, depending on experience, location, and employer.
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Machine Learning Engineer

Bespoke Labs

Houston, TX • On-site

Full-time

Re-posted 23 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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