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Remote Senior Machine Learning Engineer Jobs in Parlin, NJ

Spotify

Machine Learning Engineer

New York, NY · On-site +1

$170K - $212K/yr

We're looking for a Machine Learning Engineer to help us build systems that more accurately understand the performance that promotion can have, giving customers actionable insights for building their ...

Spotify

Machine Learning Engineer

New York, NY · On-site +1

$170K - $212K/yr

We're looking for a Machine Learning Engineer to help us build systems that more accurately understand the performance that promotion can have, giving customers actionable insights for building their ...

Capital One

Lead Machine Learning Engineer

New York, NY · On-site +1

$112K - $147K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Capital One

Lead Machine Learning Engineer

Manhattan, NY · On-site +1

$112K - $148K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Headspace

Staff Machine Learning Engineer

New York, NY · On-site +1

$179K - $224K/yr

About the Staff Machine Learning Engineer at Headspace: The AI & Machine Learning group at ... City, NY (remote), and Seattle, WA (remote). Candidates must permanently reside in the US ...

Etsy

Senior Staff Machine Learning Engineer

Brooklyn, NY · On-site +1

$245K - $319K/yr

We are looking for a Senior Staff Software Engineer, Machine Learning to be a pivotal technical ... This position will be in Brooklyn, NY or for remote candidates based in the United States. Etsy is ...

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All JobsMachine Learning Engineer JobsRemote Senior Machine Learning Engineer JobsRemote Senior Machine Learning Engineer Jobs in Parlin, NJ

Remote Senior Machine Learning Engineer information

See Parlin, NJ salary details

$77.7K

$147.6K

$197.7K

How much do remote senior machine learning engineer jobs pay per year?

As of Jul 7, 2026, the average yearly pay for remote senior machine learning engineer in Parlin, NJ is $147,554.00, according to ZipRecruiter salary data. Most workers in this role earn between $126,100.00 and $166,300.00 per year, depending on experience, location, and employer.

How do Remote Senior Machine Learning Engineers typically collaborate with cross-functional teams despite working remotely?

Remote Senior Machine Learning Engineers often work closely with data scientists, product managers, and software engineers using digital collaboration tools such as Slack, Jira, and video conferencing platforms. Regular virtual meetings and code reviews are standard practices to ensure alignment on project goals and to facilitate knowledge sharing. Clear communication, proactive documentation, and adaptability to different time zones are key to effective teamwork in a remote environment. This structure allows for flexibility while maintaining strong collaboration and project momentum.

What engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data modeling, and working at large tech companies or in specialized industries can earn salaries approaching or exceeding $500,000 annually, often including bonuses and stock options. Such compensation typically requires a strong educational background, a track record of impactful projects, and expertise in tools like TensorFlow or PyTorch.

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

AspectRemote Senior Machine Learning EngineerRemote Data Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models, collaborates with engineering teamsAnalyzes data, builds statistical models, provides insights
Employer & Industry UsageTech companies, startups, AI-focused firmsResearch institutions, tech companies, finance, healthcare

Remote Senior Machine Learning Engineers focus on designing, building, and deploying ML models, often working closely with engineering teams. Data Scientists analyze data and develop insights, but may not always deploy models. Both roles require strong technical skills and are highly sought after in tech industries, but their core responsibilities differ.

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

To thrive as a Remote Senior Machine Learning Engineer, you need deep expertise in machine learning algorithms, statistical analysis, and strong programming skills (often in Python or similar languages), typically supported by a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms (AWS, GCP, or Azure), and experience with data engineering pipelines are commonly required, along with certifications like TensorFlow Developer or AWS Machine Learning Specialty. Excellent problem-solving, communication, and self-management skills help you collaborate remotely, lead projects, and explain complex models to stakeholders. These skills and qualities are vital for building scalable ML solutions, ensuring effective teamwork across distributed environments, and delivering impactful results.

What does a Remote Senior Machine Learning Engineer do?

A Remote Senior Machine Learning Engineer designs, develops, and deploys machine learning models and systems while working from a location outside the traditional office. They collaborate with cross-functional teams, analyze large datasets, build scalable algorithms, and often mentor junior engineers. Their work helps organizations automate processes, gain insights, and improve products or services using data-driven approaches. Senior engineers are also responsible for ensuring model performance, reliability, and integration into production environments. Working remotely, they use various communication and collaboration tools to stay connected with their team.

What engineers make $300,000 a year?

Senior machine learning engineers can earn $300,000 or more annually, especially with extensive experience, advanced skills in deep learning and data modeling, and work at large tech companies or in specialized industries. Compensation often includes base salary, bonuses, and stock options, particularly in high-demand markets.

Will MLE be replaced by AI?

As a Senior Machine Learning Engineer, the role involves designing, developing, and maintaining AI systems, which currently require human expertise. While AI tools can automate certain tasks, the need for skilled professionals to interpret data, ensure ethical use, and improve models remains essential. AI is more likely to augment rather than replace the responsibilities of MLEs in the foreseeable future.

What engineers make $200,000 a year?

Senior machine learning engineers often earn $200,000 or more annually, especially with extensive experience, advanced skills in deep learning and data modeling, and proficiency with tools like TensorFlow or PyTorch. Compensation can vary based on industry, location, and company size, with some roles in tech giants or specialized fields reaching or exceeding this level.
What are popular job titles related to Remote Senior Machine Learning Engineer jobs in Parlin, NJ? For Remote Senior Machine Learning Engineer jobs in Parlin, NJ, the most frequently searched job titles are:
What cities near Parlin, NJ are hiring for Remote Senior Machine Learning Engineer jobs? Cities near Parlin, NJ with the most Remote Senior Machine Learning Engineer job openings:
Remote Senior Machine Learning Engineer 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 11 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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