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Director Google Machine Learning Engineer Jobs in Auburn, MA

Bespoke Labs

Machine Learning Engineer

Worcester, MA · 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 ...

Nucs AI

Machine Learning Scientist

Berlin, MA · On-site +1

  • Medical

  • Vision

The Opportunity Nucs AI is looking for a Machine Learning Scientist to deepen our ML research ... Strong programming skills in Python and deep learning frameworks (PyTorch preferred) * Experience ...

Waters Corporation

AI Engineer II

Milford, MA · On-site

$106K - $146K/yr

Overview The AI Engineer II supports the design, development, and deployment of AI-powered ... Build solutions leveraging large language models (LLMs), machine learning models, and knowledge ...

Waters Corporation

AI Engineer II

Milford, MA

$106K - $146K/yr

Overview TheAI Engineer IIsupportsthe design, development, and deployment of AI-powered ... Build solutions leveraging large language models (LLMs), machine learning models, and knowledge ...

Waters Corporation

AI Engineer II

Milford, MA · On-site

$106K - $146K/yr

TheAI Engineer IIsupportsthe design, development, and deployment of AI-powered applications and ... Build solutions leveraging large language models (LLMs), machine learning models, and knowledge ...

Waters Corporation

AI Engineer II

Milford, MA · On-site

$106K - $146K/yr

Overview The AI Engineer II supports the design, development, and deployment of AI-powered ... Build solutions leveraging large language models (LLMs), machine learning models, and knowledge ...

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Director Google Machine Learning Engineer information

See Auburn, MA salary details

$36K

$91.9K

$140.9K

How much do director google machine learning engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for director google machine learning engineer in Auburn, MA is $91,870.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,500.00 and $105,900.00 per year, depending on experience, location, and employer.

What are the most commonly searched types of Google Machine Learning Engineer jobs in Auburn, MA?

The most popular types of Google Machine Learning Engineer jobs in Auburn, MA are:

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The top searched job categories for Director Google Machine Learning Engineer jobs in Auburn, MA are:

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Director Google Machine Learning Engineer jobs near you
Infographic showing various Director Google Machine Learning Engineer job openings in Auburn, MA as of June 2026, with employment types broken down into 1% As Needed, 55% Full Time, 42% Part Time, and 2% Contract. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $91,870 per year, or $44.2 per hour.

Machine Learning Engineer

Bespoke Labs

Worcester, MA • On-site

Full-time

Re-posted 2 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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