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Manager Data Analyst Machine Learning Jobs in Vermont

Pwc

ERP AI Engineer - Senior Associate

Montpelier, VT · On-site

$77K - $202K/yr

... Analytics Cloud, Oracle Data Integrator, Oracle Data Visualization, and Oracle Machine Learning. Focused on relationships, you are building meaningful client connections, and learning how to manage ...

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Manager Data Analyst Machine Learning information

What is the difference between Manager Data Analyst Machine Learning vs Data Scientist?

AspectManager Data Analyst Machine LearningData Scientist
Required CredentialsBachelor's/Master's in Data Science, Analytics, or related; experience in machine learningBachelor's/Master's/PhD in Data Science, Statistics, or related; strong programming skills
Work EnvironmentTeam leadership, project management, cross-department collaborationResearch, model development, data exploration
Employer & Industry UsageBusiness analytics, tech companies, finance, healthcareTech firms, research institutions, consulting

While both roles involve data analysis and machine learning, the Manager Data Analyst Machine Learning focuses on leading teams and managing projects, whereas Data Scientists primarily develop models and perform in-depth data research.

What cities in Vermont are hiring for Manager Data Analyst Machine Learning jobs? Cities in Vermont with the most Manager Data Analyst Machine Learning job openings:
Manager Data Analyst Machine Learning jobs near you

Machine Learning Engineer

Bespoke Labs

Essex Junction, VT • On-site

Full-time

Posted 3 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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