TL;DR

ML Engineer (AI/NLP): Developing and optimizing large-scale NLP, computer vision, and speech recognition models for cybersecurity with an accent on accuracy, latency, throughput, and end-to-end ML pipeline design. Focus on collaborating with diverse product and engineering teams, leading ML-based initiatives, and mentoring junior team members to drive innovation and customer impact.

Location: Hybrid role based in Columbus, Ohio, USA. Employees are expected to come to the office at least two days per week.

Salary: $120,000–$180,000 USD

Company

%hirify_global% is a well-established company committed to growth in the rapidly evolving cybersecurity industry, developing proprietary ML products.

What you will do

Research, design, develop, and maintain state-of-the-art NLP, computer vision, and speech recognition models.
Train and evaluate models, optimizing for accuracy, latency, and throughput.
Collaborate with diverse teams (Product, Engineering, Marketing, Customer Success, Sales) to develop and deploy customer-facing predictive models.
Design and implement end-to-end data and ML pipelines, including data sourcing, cleaning, feature engineering, and model monitoring.
Lead ML-based initiatives, provide recommendations for scalability, and influence software architecture decisions.
Mentor and guide junior team members, championing best practices and introducing fresh ideas.
Own, shape, and prioritize your work with little to no oversight from management.

Requirements

1+ years of experience developing large-scale NLP, computer vision, or speech recognition systems deployed to production environments.
Solid programming skills in Python, with experience using PyTorch, NLTK, Spacy, OpenCV, Tesseract, and Huggingface.
Strong foundational knowledge in linear algebra, stochastic optimization, and probability theory.
Ph.D. or Master’s degree in a quantitative field with at least 1 year of industry experience, or Bachelor’s degree with at least 3 years of industry experience applying advanced ML techniques.
Extensive experience developing natural language processing, computer vision, or predictive statistical models on large datasets.
Proficiency with AWS data pipeline technologies such as Kinesis, Lambda, S3, Elasticsearch, EMR (Hadoop, Spark) and ETL tools like Apache Airflow.
Proficiency using code orchestration tools like Apache Airflow, DVC, or SageMaker Pipelines.
Ability to work in a hybrid model, requiring office attendance at least two days per week in Columbus, Ohio, USA.

Nice to have

Experience working with large (more than 2 million training examples) and highly unbalanced datasets.

Culture & Benefits

Hybrid working model that champions both collaborative teamwork and individual flexibility.
Opportunities for formal and on-the-job learning to accelerate your career journey.
Comprehensive benefits package supporting a healthy lifestyle for employees and their families.
Dynamic environment recognizing and celebrating achievements.
Work in cross-functional teams to build knowledge and ensure impactful contributions.