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Remote Machine Learning Jobs in Bridgewater, NJ (NOW HIRING)

Mercor

Senior Machine Learning Engineer

New York, NY · Remote

$70 - $100/hr

Remote Commitment: 40 hours/week Role Responsibilities * Guide research and engineering teams to ... experience in Machine Learning , Data Science , Software Engineering , Computer Science ...

Etsy

Senior Staff Machine Learning Engineer

New York, NY · On-site +1

$245K - $319K/yr

This position will be in Brooklyn, NY or for remote candidates based in the United States. Etsy is ... A foundational and practical understanding of machine learning principles and the critical steps ...

SeatGeek

Senior Machine Learning Engineer

New York, NY · On-site +1

$145K - $209K/yr

You enjoy working with a diverse group of people with different experiences and take pride in mentoring and learning from others Our stack You do not need experience with all of these, but we thought ...

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All JobsMachine Learning JobsRemote Machine Learning JobsRemote Machine Learning Jobs in Bridgewater, NJ

Remote Machine Learning information

See Bridgewater, NJ salary details

$26.1K

$43.5K

$90K

How much do remote machine learning jobs pay per year?

As of Jul 14, 2026, the average yearly pay for remote machine learning in Bridgewater, NJ is $43,530.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,200.00 and $47,000.00 per year, depending on experience, location, and employer.

What engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data modeling, and often working at large tech companies or in specialized industries can earn salaries approaching or exceeding $500,000 annually. Compensation may include base salary, bonuses, and stock options, especially in high-demand markets.

What are the key skills and qualifications needed to thrive as a Remote Machine Learning Engineer, and why are they important?

To thrive as a Remote Machine Learning Engineer, you need a strong background in mathematics, statistics, programming (often Python), and experience with machine learning frameworks, typically supported by a relevant degree. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms (like AWS or GCP), and version control systems is crucial. Strong problem-solving abilities, self-management, and effective virtual communication distinguish top performers in remote settings. These competencies ensure the engineer can build effective models, collaborate across distributed teams, and deliver impactful solutions independently.

How to make 2000 a week working from home?

Remote machine learning professionals can earn $2,000 or more weekly by taking on high-paying freelance projects, consulting roles, or working for companies that offer remote positions with competitive salaries. Building specialized skills in programming, data analysis, and tools like Python, TensorFlow, or cloud platforms can increase earning potential. Consistent work, a strong portfolio, and networking are key to reaching this income level from home.

What Are Remote Machine Learning Jobs?

Machine learning is a method of analyzing data via automating analytical model building. The premise is that systems can learn from data. Machine learning positions include machine learning engineer, computer vision engineer, and senior deep learning engineer. In a remote machine learning job, you work from home in a branch of artificial intelligence performing duties related to computational processing and data. Your goal is to design models that solve business problems, such as helping organizations avoid unknown risks or find profitable opportunities. Your responsibilities include maintaining data pipelines, performing model research and implementation, building machine learning systems, and onboarding new utilities.

What is a remote machine learning job?

A remote machine learning job involves working with algorithms, data, and models to develop predictive systems or automate tasks, all while working from a location outside of a traditional office setting. Professionals in this role use techniques from statistics and computer science to analyze data, train machine learning models, and deploy solutions for real-world applications. Remote machine learning jobs can span various industries, including technology, healthcare, finance, and e-commerce. These roles typically require strong programming skills, knowledge of machine learning frameworks, and the ability to communicate findings effectively with team members or stakeholders. Working remotely offers flexibility, but also requires discipline and self-motivation to succeed.

What are some effective strategies for collaborating with team members while working remotely as a Machine Learning Engineer?

Collaboration in a remote Machine Learning role often relies on clear communication through digital tools such as Slack, Zoom, and project management platforms like Jira or Asana. Regular check-ins and stand-up meetings help keep everyone aligned on project goals and timelines. Sharing code and models via version control systems (like Git) and using collaborative notebooks (such as JupyterHub or Google Colab) are also common practices. Building strong documentation habits and proactively seeking feedback can help ensure smooth teamwork and project success, even across different time zones.

What is the difference between Remote Machine Learning vs Data Scientist?

AspectRemote Machine LearningData Scientist
Required CredentialsBachelor's/Master's in CS, ML certificationsBachelor's/Master's in CS, Statistics, or related field
Work EnvironmentRemote, collaborative teams, tech companiesRemote or on-site, diverse industries, analytics focus
Industry UsageTech, AI startups, researchFinance, healthcare, e-commerce, tech
Search & Comparison IntentOften compared for technical roles in AI/MLBroader data analysis roles, but overlapping skills

Remote Machine Learning specialists focus on developing algorithms and models primarily in tech environments, often requiring advanced programming and ML knowledge. Data Scientists analyze data to extract insights, sometimes utilizing ML techniques. While both roles share skills and credentials, Remote Machine Learning emphasizes model development, whereas Data Scientists focus on data analysis and interpretation.

Are there remote machine learning jobs?

Yes, remote machine learning jobs are widely available across various industries, often requiring skills in programming, data analysis, and familiarity with tools like Python, TensorFlow, or PyTorch. Many companies offer flexible schedules and remote work options for qualified candidates, especially in tech and research sectors.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and deploy AI models, and their role involves understanding algorithms, data preprocessing, and model optimization. While AI automation tools can handle certain tasks, MLEs are essential for creating, fine-tuning, and maintaining complex AI systems, making complete replacement unlikely in the near term.
More about Remote Machine Learning jobs
What are popular job titles related to Remote Machine Learning jobs in Bridgewater, NJ? For Remote Machine Learning jobs in Bridgewater, NJ, the most frequently searched job titles are:
What cities near Bridgewater, NJ are hiring for Remote Machine Learning jobs? Cities near Bridgewater, NJ with the most Remote Machine Learning job openings:
Remote Machine Learning jobs near you
Infographic showing various Remote Machine Learning job openings in Bridgewater, NJ as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 22% Part Time, 1% Temporary, and 2% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $43,530 per year, or $20.9 per hour.
Machine Learning Engineer (LLM / Personalization)

Machine Learning Engineer (LLM / Personalization)

Qloo

New York, NY • On-site, Remote

$100K - $120K/yr

Full-time

Medical, Retirement, PTO

Re-posted 15 hours ago

Job description

About Us

At Qloo, our cutting-edge Taste AI technology leverages extraordinary amounts of data-over half a billion records of public figures, places, music artists, media, brands, and more, plus a globe-spanning consumer behavior and sentiment database-to unearth deep insights about consumer preferences.

From understanding global travel trends to curating the perfect restaurant recommendation based on your unique tastes, our Taste AI engine sifts through the noise to find the signals that matter.

And the best part? Qloo's API suite is powered by cultural entities, not personal identities-ensuring our insights are derived without relying on personally identifiable information.

As we expand our investment in LLMs and AI agents, we are building the next generation of intelligent systems that combine generative models with structured taste intelligence-bringing reliability, explainability, and real-world grounding to AI applications.

Role Overview

As a Machine Learning Engineer reporting to the LLM Research Lead, you will operate at the intersection of large language models, recommendation systems, and Qloo's proprietary taste graph.

You will work closely with Research and Data Engineering teams to design and deploy systems that integrate LLMs with structured cultural intelligence. This includes building production-ready ML systems, experimenting with new model architectures, and developing novel approaches to grounding generative AI in real-world data.

This role is ideal for someone who enjoys both research-adjacent work and shipping production systems-and wants to shape how LLMs interact with structured knowledge at scale.

Responsibilities
  • Design, build, and deploy machine learning models and systems that power personalization, recommendation, and taste understanding

  • Develop and productionize LLM-powered features, including retrieval-augmented generation (RAG), agent workflows, and prompt / tool orchestration

  • Integrate LLMs with Qloo's structured entity graph and embedding systems to improve accuracy, relevance, and explainability

  • Experiment with and evaluate modern ML approaches (transformers, embedding models, ranking systems, hybrid recommenders)

  • Collaborate with Data Engineering to leverage large-scale datasets for LLM pipelines

  • Contribute to model evaluation frameworks and optimize model performance, cost, and latency in production environments

  • Stay up-to-date with the latest advancements in LLMs, recommendation systems, and applied ML-and bring those insights into production

Qualifications

  • Strong experience in Python and machine learning frameworks (e.g., PyTorch, CUDA, Metaflow/Kubeflow, etc)

  • Experience working with large language models (LLMs), including APIs (OpenAI, Anthropic, etc) and/or open-source models (Hugging Face)

  • Familiarity with retrieval systems, embeddings, vector search, or recommendation systems

  • Experience building and deploying ML systems in production environments

  • Solid understanding of data pipelines (Airflow) and working with large-scale datasets (e.g., Spark, S3, SQL)

  • Experience with AWS or similar cloud platforms

  • Experience working in AI-native development workflows, including heavy use of tools like Claude Code, Cursor, or similar

  • Strong problem-solving skills and ability to work across both research and engineering domains

  • Prior experience in a startup or fast-paced environment

We Offer
  • Competitive salary and benefits package, including health insurance, retirement plan, and paid time off

  • The opportunity to shape how LLMs and structured data systems work together in real-world applications

  • A collaborative, low-ego work environment where your ideas are valued and your contributions are visible

  • Direct exposure to cutting-edge work at the intersection of generative AI and large-scale recommendation systems

  • Flexible work arrangements (remote and hybrid options) and a healthy respect for work-life balance

$100,000 - $120,000 a year
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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