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Remote Machine Learning Jobs in New Rochelle, NY

Finch Legal Inc

Machine Learning Engineer

Manhattan, NY · On-site +1

$180K - $280K/yr

As a Machine Learning Engineer at Finch, you'll own the full lifecycle of AI systems, from ... for a remote-first role - this one is 4 days/week in our NYC office. Compensation The expected ...

Nexxen

Senior Machine Learning Engineer

Manhattan, NY · On-site +1

$180K - $220K/yr

Sr. Machine Learning Engineer Flexible advertising, unified by data. Nexxen empowers advertisers, agencies, publishers, and broadcasters around the world to utilize data and advanced TV in the ways ...

Career Renew

Senior Machine Learning Engineer

New York, NY · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ...

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 ...

Etsy

Senior Staff Machine Learning Engineer

Brooklyn, 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 ...

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All JobsRemote Machine Learning JobsRemote Machine Learning Jobs in New Rochelle, NY

Remote Machine Learning information

See New Rochelle, NY salary details

$26.2K

$43.8K

$90.6K

How much do remote machine learning jobs pay per year?

As of Jun 10, 2026, the average yearly pay for remote machine learning in New Rochelle, NY is $43,821.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,400.00 and $47,300.00 per year, depending on experience, location, and employer.

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.

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.

Is ML full of coding?

Machine Learning (ML) roles often involve significant coding, especially in programming languages like Python or R, to develop algorithms and models. However, some positions focus more on data analysis, feature engineering, or model evaluation, which may require less coding but still involve technical skills and understanding of ML concepts.
More about Remote Machine Learning jobs
What are popular job titles related to Remote Machine Learning jobs in New Rochelle, NY? For Remote Machine Learning jobs in New Rochelle, NY, the most frequently searched job titles are:
What cities near New Rochelle, NY are hiring for Remote Machine Learning jobs? Cities near New Rochelle, NY with the most Remote Machine Learning job openings:
Remote Machine Learning jobs near you
Machine Learning Engineer / Data Scientist

Machine Learning Engineer / Data Scientist

Fusemachines

New York, NY • On-site, Remote

$100K - $120K/yr

Full-time

Posted 14 days ago

Job description

About Fusemachines
Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients' AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
Type: Full-time, RemoteRole Overview
We're hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You'll work across the ML lifecycle-from problem framing and data exploration to model development, evaluation, deployment, and monitoring-often in partnership with client stakeholders and internal delivery teams.
You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems.Key Responsibilities
  • Problem Framing & Stakeholder Partnership
    • Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.).
    • Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability).
  • Data Analysis & Feature Engineering
    • Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses.
    • Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices.
    • Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions.
  • Model Development (Core ML)
    • Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data).
    • Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation.
    • Build time series models (statistical and ML/DL approaches) and validate with proper backtesting.
    • Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness.
    • Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making.
  • Deep Learning
    • Build and train deep learning models using PyTorch or TensorFlow/Keras.
    • Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design).
  • Evaluation, Explainability, and Iteration
    • Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports.
    • Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence.
  • Productionization & MLOps (Project-Dependent)
    • Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration.
    • Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans.
  • Documentation & Communication
    • Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders.
    • Create documentation and lightweight demos that make results actionable.
Success in This Role Looks Like
  • You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency).
  • Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring.
  • Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly.
Required Qualifications
  • 3-8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior).
  • Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent).
  • Strong SQL skills (joins, window functions, aggregation, performance awareness).
  • Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset.
  • Hands-on experience across multiple model types, including:
    • Classification & regression
    • Time series forecasting
    • Clustering/segmentation
  • Experience with deep learning in PyTorch or TensorFlow/Keras.
  • Strong problem-solving skills: ability to work with ambiguous goals and messy data.
  • Clear communication skills and ability to translate analysis into decisions.
Preferred Qualifications
  • Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows).
  • Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining).
  • Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks).
  • Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker).
  • Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls).
  • Consulting or client-facing delivery experience.

Certifications (Strong Plus)
Candidates with at least one relevant certification are especially encouraged to apply:
  • Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks)
  • Databricks certifications (Data Scientist, Data Engineer, or related)
Nice-to-Have
  • Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests).
  • Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products.
  • Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability.

Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
Important: Immigration Sponsorship Policy
Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including:
  • Direct Company Sponsorship: Such as H-1B, J-1, or TN visas.
  • Employer of Record: Listing Fusemachines as the immigration employer on any government documentation.
  • Written Documentation: Providing letters or other support for any work authorization (e.g., OPT, STEM OPT, CPT).

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