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Remote Applied Scientist Machine Learning Jobs in New York

Data Scientist

New York, NY ยท Remote

$130K - $135K/yr

Role: Data Scientist Location: Remote, however travel might be required as per business ... Strong knowledge of programming languages with a focus on machine learning and advanced analytics ...

Senior Machine Learning Engineer (Remote)

New York, NY ยท On-site +1

$114K - $157K/yr

We are looking for an outstanding machine learning engineer to join our team! The role will provide ... As an integral part of the squad, you will collaborate with research scientists, data scientists ...

Lead Data Scientist

New York, NY ยท Remote

$110K - $140K/yr

Design and implement statistical models, machine learning algorithms, predictive analytics models ... Applicants should have a Masters, PhD, or advanced training in applied mathematics, engineering ...

Showing results 21-40

Remote Applied Scientist Machine Learning information

What does a remote applied scientist machine learning do?

A Remote Applied Scientist in Machine Learning develops and implements machine learning models to solve real-world problems, often from a location outside of a traditional office. Their work involves analyzing large datasets, designing algorithms, and collaborating with teams to deploy scalable solutions. They may also conduct experiments to improve model performance and stay up to date with the latest research in the field. Communication and documentation are important, as they often work with cross-functional teams remotely.

What are the key skills and qualifications needed to thrive as a remote applied scientist machine learning?

To thrive as a Remote Applied Scientist in Machine Learning, you need a strong background in mathematics, statistics, and computer science, often supported by an advanced degree and experience in ML algorithm development. Familiarity with programming languages like Python or R, machine learning frameworks such as TensorFlow or PyTorch, and tools for data processing and cloud computing is essential. Exceptional problem-solving ability, communication, and self-motivation are key soft skills for collaborating remotely and driving projects forward. These skills ensure you can independently design, implement, and communicate impactful machine learning solutions in a distributed work environment.

What can I expect in terms of collaboration and communication when working as a remote applied scientist machine learning?

As a Remote Applied Scientist in Machine Learning, you will frequently collaborate with cross-functional teams, including data engineers, product managers, and software developers. Communication typically takes place via video calls, chat platforms, and shared documentation, so strong written and verbal communication skills are essential. You may participate in regular virtual stand-ups, sprint planning, and code reviews to align on project goals and share progress. Remote work environments emphasize proactive communication and self-management to ensure seamless teamwork and project delivery.
What are the most commonly searched types of Applied Scientist Machine Learning jobs in New York? The most popular types of Applied Scientist Machine Learning jobs in New York are:
What cities in New York are hiring for Remote Applied Scientist Machine Learning jobs? Cities in New York with the most Remote Applied Scientist Machine Learning job openings:

Sr. Machine Learning Engineer

Canoe Intelligence

Manhattan, NY โ€ข On-site, Remote

$180K - $220K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted yesterday


Job description

COMPANY: Canoe Intelligence

WEBSITE: https://canoeintelligence.com/

TITLE: Sr. Machine Learning Engineer

LOCATION: Hybrid in New York City or London

SALARY: $180,000 - $220,000 (based on NYC, will be adjusted for geo)

The Role:

We are looking for a Senior Machine Learning Engineer to design and deploy models that make sense of highly complex, unstructured financial documents, enabling us to deliver data with unprecedented accuracy, speed, and trust. You’ll work hands-on with LLM and other ML Models, helping scale Canoe’s platform while shaping how alternative investment firms interact with their data.

What You’ll Do:

  • Design, train, and evaluate ML models for document classification, entity extraction, summarization, and information retrieval.

  • Fine-tune and optimize large language models for domain specific use cases, optimizing their performance for accuracy, efficiency, and scalability.

  • Work closely with data engineering teams to preprocess and engineer features from large datasets to enhance the performance of machine learning models.

  • Build scalable, production-ready ML services with strong observability, monitoring, and retraining capabilities.

  • Contribute to Canoe’s MLOps stack, including CI/CD for models, feature stores, evaluation frameworks, and data versioning.

  • Collaborate with product managers, software engineers, and other stakeholders to integrate machine learning models into end-to-end solutions.

  • Stay current with advancements in LLMs, Agentic AI, and ML, and translate new research into practical improvements to Canoe’s technology stack.

  • Conduct code reviews to ensure code quality and provide mentorship to junior members of the machine learning team.

What We’re Looking For:

  • Minimum of 5 years of experience in applied ML engineering, with a focus on NLP, information extraction, or LLMs.

  • Proficiency in Python and relevant machine learning libraries (e.g., TensorFlow, PyTorch).

  • Strong understanding of MLOps (Docker, Kubernetes, CI/CD for ML, experiment tracking).

  • Proficiency with AI-assisted development tools (e.g., GitHub Copilot, Claude Code agent) to accelerate software development, prototyping, testing, and deployment of ML solutions.

  • Problem-solver with a product mindset and bias toward outcomes.

  • Excellent communication skills; able to partner across engineering, product, and business teams.

  • Comfortable in fast-paced, agile startup environments.

  • Bachelor’s degree in computer science or related field.

Preferred

  • Master Degree or PhD in computer science or related field

  • Experience in training and deploying large language models.

  • Familiarity with cloud computing platforms and distributed computing.

  • Familiarity with modern ML Ops tools such as Modal, Weights and Biases, Sagemaker, etc.

  • Experience with LLM fine-tuning techniques such as LoRA, QLoRA, or parameter-efficient training frameworks (e.g., Unsloth).

What You’ll Get:

  • Medical, dental, vision benefits

  • Flexible PTO

  • 401(k)

  • Flexible work from home policy

  • Home office stipend

  • Employee Assistance Program

  • Gym/Wifi reimbursement

  • Education assistance

  • Parental Leave

Our Values:

  • Client First —> Listen, and deliver client-centric solutions

  • Be An Owner —> Take initiative, improve situations, drive positive outcomes

  • Excellence —> Always set the highest standard for yourself and others

  • Win Together —> 1 + 1 = 3

Who We Are:

Canoe is reimagining alternative investment data processes for hundreds of leading institutional investors, capital allocators, asset servicing firms and wealth managers. By combining industry expertise with the most sophisticated data capture technologies, Canoe’s technology automates the highly-frustrating, time-consuming, and costly manual workflows related to alternative investment document and data management, extraction and delivery. With Canoe, clients can refocus capital and human resources on business performance and growth, increase efficiency, and gain deeper access to their data. Canoe’s AI-driven platform was developed in 2013 for Portage Partners LLC, a private investment firm.

Canoe is an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.