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Remote Audio Machine Learning Jobs in New York (NOW HIRING)

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 ...

Reality Defender

Staff AI Engineer (Audio)

New York, NY · Remote

$185K - $235K/yr

PhD in Computer Science, Machine Learning, Signal Processing, or related field (or equivalent ... Remote Fridays * Happy Hours and other local events Compensation Range: $185K - $235K

New

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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Remote Audio Machine Learning information

What is the difference between Remote Audio Machine Learning vs Remote Audio Engineer?

AspectRemote Audio Machine LearningRemote Audio Engineer
Required CredentialsBackground in machine learning, data science, or AI; often a degree in computer science or related fieldsAudio engineering, sound design, or music production degree or certification
Work EnvironmentPrimarily focused on developing algorithms, data analysis, and model training, often in a tech or research settingRecording, mixing, editing audio, often in studios or remote production setups
Employer & Industry UsageTech companies, research labs, AI startups working on audio recognition or enhancementMusic, film, broadcasting, and media production companies

Remote Audio Machine Learning specialists focus on developing algorithms to process and analyze audio data, while Remote Audio Engineers handle the practical aspects of recording and editing sound. Both roles may collaborate but serve different functions within the audio industry.

How does a Remote Audio Machine Learning role typically collaborate with cross-functional teams, and what communication tools are commonly used?

In a Remote Audio Machine Learning position, collaboration with cross-functional teams such as software engineers, data scientists, and product managers is essential. Regular communication is maintained through tools like Slack, Zoom, and project management platforms such as Jira or Trello. Team members often participate in virtual stand-ups, sprint planning sessions, and code reviews to ensure alignment on project goals and timelines. Effective asynchronous communication and clear documentation are especially important in remote settings to keep everyone informed and foster a productive workflow.

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

To thrive as a Remote Audio Machine Learning Engineer, you need strong foundations in digital signal processing, machine learning algorithms, and programming (often Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, and audio processing libraries (e.g., LibROSA), as well as experience with cloud platforms, is highly valuable. Excellent problem-solving skills, self-motivation, and clear remote communication are essential soft skills for collaborating across distributed teams. These competencies enable the development of robust, innovative audio ML solutions while ensuring effective teamwork and project delivery in a remote setting.

What is a Remote Audio Machine Learning job?

A Remote Audio Machine Learning job involves using machine learning techniques to analyze, process, or generate audio data while working from a remote location. Professionals in this field develop algorithms for tasks such as speech recognition, music classification, noise reduction, or audio synthesis. They often work with large datasets, build and train models, and collaborate with teams online. These roles typically require skills in programming, signal processing, and experience with machine learning frameworks.
What are the most commonly searched types of Audio Machine Learning jobs in New York? The most popular types of Audio Machine Learning jobs in New York are:
What are popular job titles related to Remote Audio Machine Learning jobs in New York? For Remote Audio Machine Learning jobs in New York, the most frequently searched job titles are:
What job categories do people searching Remote Audio Machine Learning jobs in New York look for? The top searched job categories for Remote Audio Machine Learning jobs in New York are:
What cities in New York are hiring for Remote Audio Machine Learning jobs? Cities in New York with the most Remote Audio Machine Learning job openings:
Remote Audio 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 15 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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