Managing pricing and rebates shouldn’t be a hassle. Enable’s intelligent
platform is built for the speed of today’s market, eliminating disconnects
between pricing strategy and rebate execution. We help companies to increase
profitability and simplify the complex with accurate, AI-powered insights,
real-time performance monitoring, agreement optimization, and simplified rebate
management.
After securing $291M in Series A-D funding and acquiring Flintfox in 2025,
Enable is positioned for continued, significant growth. Since the launch of our
flagship product in 2016, we have been rapidly scaling our client base, product
offerings, and built a team of top-tier professionals committed to reshaping the
industry.
Want a glimpse into life at Enable? Visit our Life at Enable
[https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fenable-software%2Flife%2Flife-at-enable%2F&data=05%7C02%7CRobin.Reshwan%40enable.com%7C03454c1fbad54903097d08dd8f14e102%7C2b7354e95ae24699907ad03d45fe0053%7C0%7C0%7C638824042013006446%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=J1EMh9EIJTJv2vJh0uhElNT9vipNTnkQLa9442JwFdI%3D&reserved=0] page
to learn how you can be part of our journey.
Job Summary
We are seeking a detail-oriented engineer passionate about the foundational
elements of machine learning. This individual will play a key role in building
robust data pipelines, improving dataset quality, and streamlining model
deployment processes. The ideal candidate is a junior engineer eager to learn
how clean data and seamless operations power impactful ML solutions across the
business.
\n
Duties & Responsibilities
- Support data quality assurance by cleaning, validating, and auditing datasets
to ensure accuracy and completeness.
- Identify and resolve data anomalies, biases, and missing values in
partnership with data engineering teams.
- Assist in the development of automated validation tools using Python (pandas,
Great Expectations).
- Contribute to synthetic data creation efforts by helping design synthetic
datasets to augment training data or simulate edge cases.
- Learn techniques such as GANs, VAEs, and rule-based simulation under
mentorship.
- Test and evaluate synthetic data efficacy in model training pipelines.
- Assist with automated labeling by supporting labeling pipelines using tools
such as Snorkel, weak supervision, or active learning.
- Collaborate with annotation teams to improve labeling workflows and reduce
manual intervention.
- Provide MLOps support in deploying and monitoring models in production
environments (Azure) using tools such as Docker, Airflow, or MLflow.
- Assist in building CI/CD pipelines for automated model testing and
retraining.
- Help document processes and maintain performance dashboards.
Knowledge, Skills & Abilities (KSAs)
- Foundational knowledge of Python (NumPy, pandas) and basic SQL.
- Familiarity with data preprocessing and validation techniques, with an
interest in synthetic data tools such as Synthea or Faker.
- Curiosity and developing skills in MLOps tools such as Kubeflow or TFX and
cloud environments (AWS, GCP, Azure).
- Strong analytical and problem-solving abilities with attention to data
integrity and systematic execution.
- Effective communication skills for cross-functional collaboration across
technical and non-technical teams.
Required Education & Experience
- Bachelor’s degree in Computer Science, Data Science, or a related technical
field, or equivalent practical experience.
- Foundational experience working with data pipelines, model development, or ML
workflows through coursework or early professional projects.
Preferred Education & Experience
- Hands-on experience or academic projects involving data engineering,
synthetic data generation, or automated labeling.
- Familiarity with labeling platforms such as Label Studio or Scale AI and
workflow orchestration tools such as Airflow.
- Basic knowledge of statistical methods for data quality and bias analysis.
\n
Total Rewards:
At Enable, we’re committed to your professional development and growth. Starting
pay is determined by factors like location, skills, experience, market
conditions, and internal parity.
Salary/TCC is just one component of Enable’s total rewards package. Enable is
committed to investing in the holistic health and wellbeing of all Enablees and
their families. Our benefits and perks include, but are not limited to:
Paid Time Off: Take the time you need to relax and recharge
Wellness Benefit: Quarterly incentive dedicated to improving your health and
well-being
Comprehensive Insurance: Health and life coverage for you and your family
Retirement Plan: Build your future with our retirement savings plan
Lucrative Bonus Plan: Enjoy a rewarding bonus structure subject to company or
individual performance
Equity Program: Benefit from our equity program with additional options tied to
tenure and performance
Career Growth: Explore new opportunities with our internal mobility program
Additional Perks:
Free Food: Complimentary meals, snacks, and drinks on-site in our global
offices
Training: Access a range of workshops and courses designed to boost your
professional growth and take your career to new heights
Pets: Bring your pets to our welcoming, pet-friendly offices
According to LinkedIn's Gender Insights Report, women apply for 20% fewer jobs
than men, despite similar job search behaviors. At Enable, we’re committed to
closing this gap by encouraging women and underrepresented groups to apply, even
if they don’t meet all qualifications.
Enable is an equal opportunity employer, fostering an inclusive, accessible
workplace that values diversity. We provide fair, discrimination-free
employment, ensuring a harassment-free environment with equitable treatment.
We welcome applications from all backgrounds. If you need reasonable adjustments
during recruitment or in the role, please let us know.
