Ingersoll Rand
Website:
irco.com
Job details:
Ingersoll Rand is committed to achieving workforce diversity reflective of our communities. We are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.
Role : Backend and Algorithm Engineer.
Level: Mid-level,Individual Contributor
Experience: 4+ years
About The Role
We build a data-driven platform that optimizes compressed air systems — compressors — under real-world operating constraints, including stability, safety, and performance. The algorithms this team develops translate live sensor data into operating decisions that reduce energy consumption, lower emissions, and generate cost savings for customers.
This role carries end-to-end ownership of algorithms: from the mathematical and control logic that determines how a compressor should behave, through to the production service that executes that decision continuously and safely in the field. For this hire, we are prioritizing engineering strength. We are seeking a candidate whose existing practices already reflect a rigorous, test-first, strongly typed approach to software, so that onboarding time is spent on domain context rather than on foundational engineering habits.
Responsibilities
- Build and maintain production-grade Python services and automated pipelines to a consistently high standard of testing, type safety, and reliability.
- Design and implement optimization and control logic that translates physical and operational constraints into decisions executed by real compressors.
- Fit and calibrate models against noisy, real-world sensor data, exercising sound judgment about when a given fit or result cannot be trusted, such as in the presence of outliers, edge cases, or boundary behavior.
- Write tests prior to implementation as standard practice, using shared, reusable test fixtures rather than ad hoc, hardcoded test data.
- Carry projects from design through automated verification to production deployment, in close collaboration with a distributed core engineering team.
Required Qualifications
- 4+ years of experience building and shipping production Python systems, with demonstrated proficiency in type-hinted code and schema-first data models, as opposed to unstructured data passed between functions.
- Strong testing discipline, including test-first development, well-structured and reusable test fixtures, and a consistently high standard of test coverage as an established practice rather than an aspirational goal.
- Demonstrated ability to own a service across its full lifecycle: containerized, deployed through an automated pipeline, and operated within an orchestrated, multi-service infrastructure.
- Working knowledge of relational databases and at least one caching or eventing layer in a production environment.
- Familiarity with modern, reproducible dependency management practices for Python, in contrast to less structured approaches to package installation.
- Strong applied problem-solving ability in optimization or control logic, with demonstrated capacity to translate physical or operational constraints, such as stability, safety, or performance limits, into functioning algorithms rather than relying solely on off-the-shelf models.
- Experience fitting or calibrating models against real-world, noisy data, with sound judgment regarding the reliability of results.
- A disciplined approach to data and experimentation, including reproducible results, appropriate training and validation practices for time-ordered data, and awareness of data leakage risks.
- Strong communication skills and demonstrated ability to collaborate effectively with a distributed team across time zones.
Preferred Qualifications
- Experience with more than one cloud provider, or genuine interest in cloud migration work.
- Experience with workflow or pipeline orchestration systems, beyond request-response APIs.
- Academic or professional background in a physical or engineering discipline relevant to rotating or reciprocating machinery — thermodynamics, fluid mechanics, or mechanical/industrial systems — compressors being one example.
- Hands-on machine learning experience, which is valuable but secondary to the optimization and control skill set described above.
- An advanced degree in a quantitative or engineering field.
What We Offer
- We are all owners of the company! Stock options (Employee Ownership Program) that align your interests with the company's success.
- Yearly performance-based bonus, rewarding your hard work and dedication.
- Leave Encashments
- Maternity/Paternity Leaves
- Employee Health covered under Medical, Group Term Life & Accident Insurance
- Employee Assistance Program
- Employee development with LinkedIn Learning
- Employee recognition via Awardco
- Collaborative, multicultural work environment with a team of dedicated professionals, fostering innovation and teamwork.
Ingersoll Rand Inc. (NYSE:IR), driven by an entrepreneurial spirit and ownership mindset, is dedicated to helping make life better for our employees, customers and communities. Customers lean on us for our technology-driven excellence in mission-critical flow creation and industrial solutions across 40+ respected brands where our products and services excel in the most complex and harsh conditions. Our employees develop customers for life through their daily commitment to expertise, productivity and efficiency. For more information, visit www.IRCO.com.
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