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Machine Learning Engineer (Remote - US)

Jobgether
United States only

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning Engineer in the United States.

As a Machine Learning Engineer, you will develop and deploy cutting-edge AI solutions that transform the way complex financial and tax documents are processed. You will focus on natural language processing (NLP) for unstructured and semi-structured data, driving high-impact projects that improve automation, accuracy, and efficiency. This role offers a combination of hands-on modeling, collaboration with cross-functional teams, and mentorship of junior colleagues. You will influence software product capabilities, translate business challenges into technical solutions, and help shape the future of AI-driven data processing in the financial domain. This fully remote position allows you to contribute from anywhere while remaining closely connected to a dynamic, innovative team.

Accountabilities

  • Develop and optimize machine learning models for parsing, extracting, and categorizing information from complex financial and tax documents.
  • Collaborate with software, product, and data teams to integrate machine learning solutions into production systems.
  • Conduct statistical analyses to identify patterns, trends, and actionable insights that drive product and business improvements.
  • Lead end-to-end machine learning projects, from data acquisition and preprocessing to model deployment and monitoring.
  • Mentor and guide junior data scientists and engineers, fostering a collaborative and innovative environment.
  • Stay current with advances in NLP, NLU, and ML technologies, and apply best practices to enhance workflows and model performance.

Requirements

  • Master’s or PhD in Computer Science, Mathematics, Statistics, Data Science, or a related field (or equivalent experience).
  • 6+ years of experience in data science or machine learning, with a strong focus on NLP/NLU projects.
  • Proven ability to lead end-to-end data science project implementations.
  • Expertise in Python and machine learning frameworks/libraries such as scikit-learn, PyTorch, spaCy, and Huggingface.
  • Strong problem-solving skills with the ability to communicate complex technical results to both technical and non-technical stakeholders.
  • Preferred: experience applying NLP to financial documents, familiarity with alternative investment accounting, and deploying ML models with containerization/orchestration technologies.

Benefits

  • Unlimited Vacation Policy, plus Sick Time and Holidays
  • Paid Parental Leave
  • Fully Remote Work Opportunity
  • Comprehensive Healthcare Benefits
  • 401(k) Retirement Plan
  • Collaborative, growing startup culture with opportunities for professional development

Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.

When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.
🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
📊 It compares your profile to the job’s core requirements and past success factors to determine your match score.
🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.
🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.

The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role.
Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.

Thank you for your interest!

About the job

Apply before

Posted on

Job type

Full Time

Experience level

Mid-level

Location requirements

Hiring timezones

United States +/- 0 hours
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