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Director Google Machine Learning Engineer Jobs in Springfield, MA

Bespoke Labs

Machine Learning Engineer

Hartford, CT · 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 ...

The Hartford

AI Machine Learning Engineer

Hartford, CT · Hybrid

$100K - $151K/yr

The Hartford is seeking AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Global Specialty Applied AI team. The Hartford is developing industryleading AI and ...

The Hartford

Senior AI Machine Learning Engineer

Hartford, CT · Hybrid

$123K - $162K/yr

As a Senior Machine Learning Engineer , you will play a critical role in designing, building, and operationalizing productiongrade AI solutions-partnering closely with product, engineering, and ...

The Hartford

Sr AI Machine Learning Engineer

Hartford, CT · Hybrid

$117K - $175K/yr

The Hartford is seeking Senior AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Global Specialty Applied AI team. The Hartford is developing industryleading ...

Deloitte

Google AI Lead Architect

Hartford, CT · On-site

$55.75 - $76.50/hr

Preferred : • Google Professional Machine Learning Engineer certification or the equivalent ML certification. • Master's degree in technology-related discipline. • 2+ years' leading high ...

Deloitte

AI Solutions Architect

Hartford, CT · On-site

$63.50 - $83.75/hr

Certifications in artificial intelligence, machine learning, or cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Microsoft ...

Varsity Tutors

Machine Learning Tutor

Hartford, CT · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Northampton, MA · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

NAVA Software Solutions

Senior Data Engineer

Bloomfield, CT · On-site

$105K - $143K/yr

AWS, Azure, google cloud) * 6+ years of working in an object-oriented language: Java or Scala and ... Understanding of Machine learning frame works (e.g. Scikit learning, Scipy) * Understanding and 1+ ...

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Director Google Machine Learning Engineer information

See Springfield, MA salary details

$35.9K

$91.6K

$140.5K

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

As of Jul 8, 2026, the average yearly pay for director google machine learning engineer in Springfield, MA is $91,611.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,200.00 and $105,600.00 per year, depending on experience, location, and employer.

Is L7 senior at Google?

At Google, L7 is considered a senior-level position, typically involving significant technical expertise and leadership responsibilities. It is often associated with senior engineers or managers, depending on the role and team structure.

What engineer makes $500,000 a year?

A senior Google Machine Learning Engineer or Director level in large tech companies can earn $500,000 or more annually, often including base salary, bonuses, and stock options. These roles typically require extensive experience, advanced skills in machine learning and AI, and often involve leadership responsibilities and high-impact projects.

How much does a Google Engineering director make?

A Google Engineering Director typically earns between $200,000 and $300,000 annually, with total compensation including bonuses and stock options often exceeding this range. Compensation varies based on experience, location, and performance, and senior roles may include additional benefits and incentives.

Will MLE be replaced by AI?

As a Google Machine Learning Engineer, the role involves developing and deploying AI models, but AI is a tool that enhances rather than replaces MLE work. MLEs focus on designing, optimizing, and maintaining machine learning systems, which require expertise in data science, programming, and domain knowledge that AI cannot fully replicate. The role is expected to evolve with advancements in AI, emphasizing collaboration with AI systems rather than replacement.
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Director Google Machine Learning Engineer jobs near you

Machine Learning Engineer

Bespoke Labs

Hartford, CT • On-site

Full-time

Posted 21 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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