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Executive Full Stack Machine Learning Engineer Jobs in Cambridge, MD

Bespoke Labs

Machine Learning Engineer

Seaford, DE

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

Seaford, DE · 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 ...

Booz Allen Hamilton

Software Engineer

Lexington Park, MD · On-site

$69K - $158K/yr

As a full stack developer, you can resolve a problem with a complete end-to-end solution in a fast, agile environment. If you're looking for the chance to not just develop software, but to help ...

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All JobsFull Stack Machine Learning Engineer JobsExecutive Full Stack Machine Learning Engineer JobsExecutive Full Stack Machine Learning Engineer Jobs in Cambridge, MD

Executive Full Stack Machine Learning Engineer information

See Cambridge, MD salary details

$41.8K

$126.6K

$179K

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

As of Aug 5, 2026, the average yearly pay for executive full stack machine learning engineer in Cambridge, MD is $126,648.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,300.00 and $148,500.00 per year, depending on experience, location, and employer.

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

AspectExecutive Full Stack Machine Learning EngineerData Scientist
CredentialsBachelor's/Master's in CS, Engineering, or related; often requires experience in ML and full stack developmentBachelor's/Master's in Data Science, Statistics, or related; strong analytical and statistical skills
Work EnvironmentDevelops end-to-end ML solutions, integrates backend and frontend, collaborates with engineering teamsAnalyzes data, builds models, visualizes insights, often in research or analytics teams
Industry UsageUsed in tech companies, startups, and enterprises deploying ML productsCommon in research institutions, analytics firms, and data-driven organizations

The Executive Full Stack Machine Learning Engineer focuses on building and deploying complete ML solutions, combining software engineering and data science skills. In contrast, Data Scientists primarily analyze data and develop models without necessarily handling full stack development. Both roles require strong technical credentials but differ in scope and daily tasks.

Executive Full Stack Machine Learning Engineer jobs near you
Infographic showing various Executive Full Stack Machine Learning Engineer job openings in Cambridge, MD as of July 2026, with employment types broken down into 89% Full Time, 9% Part Time, and 2% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $126,648 per year, or $60.9 per hour.

Machine Learning Engineer

Bespoke Labs

Seaford, DE

Full-time

Re-posted 19 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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