Machine Learning / Arlington, Va, Remote

Senior Machine Learning Engineer (ML, NLP)

Based in Arlington, Va., Interos’ breakthrough SaaS platform leverages artificial intelligence and machine learning to model the entire business ecosystems of companies into a living global map. Users can drill down to any single supplier, anywhere – helping businesses and government organizations reduce risk, avoid operational disruptions, and achieve dramatically superior resilience and performance. We have seen hypergrowth in the past 3 years and continue to expand our team across the US and several countries in Europe.

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Founded in 2005 by supply chain risk management expert Jennifer Bisceglie, we have grown into the largest and most influential player in the emerging operational resilience space. This past July we achieved $100 million dollars in Series C funding led by the investment firm Night Dragon, with additional participation from Series A and B investors Kleiner Perkins and Venrock. This latest round of funding brings Interos’ valuation to $1billion, earning us unicorn status.

Based in Arlington, Va., Interos’ breakthrough SaaS platform leverages artificial intelligence and machine learning to model the entire business ecosystems of companies into a living global map. Users can drill down to any single supplier, anywhere – helping businesses and government organizations reduce risk, avoid operational disruptions, and achieve dramatically superior resilience and performance. We have seen hypergrowth in the past 3 years and continue to expand our team across the US and several countries in Europe.

The Opportunity:

Interos is a product-oriented company. We are building the world's first fully connected knowledge graph of commercial entities to understand global supply chain risk for our customers. The data to do this does not exist, so we are making it.

Interos is looking for world-class machine learning engineers, experts on extracting structured information from unstructured text. We want to identify real-time risk across every company in our customers' ecosystems and beyond.

You want to solve problems for which you can't just Google answers. You want to deal with real big data in actual products that real people use. You want exposure to cutting edge tech, including machine learning at scale, graph databases, and predictive modeling. And you want it all in a high-growth startup.

The selected applicants will have the opportunity to experiment with large data sets, cutting edge language models, and be supported by a crack team of software engineers and data analysts ready to create the best training data possible and help operationalize your models on AWS infrastructure. This job is an individual contributor position, but you will have significant input into research and product development decisions and serve as a key technical leader within the company.

You will contribute to industry-leading projects using your software engineering, data engineering, and machine learning skills to transfer ideas into solutions for some of the most challenging technical problems that will delight customers. You will be a key driver in taking something from an idea to an experiment to a prototype and finally to a live production system.

Essential Functions/duties:

  • Use machine learning, data mining, statistical techniques, and others to create actionable, meaningful, and scalable solutions for business problems.
  • Analyze and extract relevant information from large amounts of data and derive useful insights.
  • Serve as a subject matter expert for your area of ML expertise, and partner with functional experts across the company to bring that expertise to bear on some of the hardest and most exciting problems in this space.
  • Serve as key technical resource for more junior machine learning engineers, helping them solve the most difficult problems.
  • Provide technical guidance to product teams on the choice of machine learning approaches appropriate for a task.
  • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation, and model implementation.
  • Develop and enhance the predictive capability of in-house developed supply chain risk models for use by some of the biggest enterprises in the world.
  • Work with MLOps to deliver production systems with your models.

Qualifications:

  • MS or PhD in Machine Learning, Data Science, Computer Science, Statistics, or Applied Mathematics.
  • 5+ years of hands-on experience in machine learning, predictive modeling, and analysis.
  • Technical skills in machine learning, deep learning and natural language processing with the ability to explain and present complex ideas.
  • Experience designing, implementing, and maintaining APIs as a service for your team and customers.
  • A preference for writing mature and maintainable software in Python.
  • Familiarity with software engineering best practices – including unit tests, code review, version control, production monitoring, etc.
  • Solid understanding of development lifecycle (design, test, implement, document) and release management using modern collaboration tools.
  • Experience with databases and data structuring/warehousing.
  • Strong verbal and written communication skills, including the ability to interact effectively with colleagues of varying technical and non-technical abilities.
  • Eligibility to obtain a security clearance is preferred.

Stuff That Really Impresses Us:

  • Experience putting ML models into a large high-availability production environment.
  • Contributions to open source projects including the NLP/NLU space.
  • Good experience creating entity, relationship, and event extraction models.
  • Great sense of humor.
  • Positive, good-natured, and generally pleasant to be around.
  • Demonstrated commitment to building a diverse and inclusive culture.
  • A sense of adventure.

Vaccination Requirement:

Interos is required to comply with Federal Executive Order 14042 mandating employees be vaccinated against COVID-19. All Interos employees must be fully vaccinated* against COVID-19, unless you have a legally valid medical or religious reason. Any requests for exemptions based on medical or sincerely held religious beliefs will be evaluated confidentially by our Chief People Officer after hiring.

*Fully vaccinated means an individual must have received their second dose of the Moderna or Pfizer vaccine or the single dose of the Johnson & Johnson vaccine.

Benefits:

  • Comprehensive Health & Wellness package (Medical, Dental and Vision)
  • 10 Paid Holiday Days Off
  • Flexible Paid Time Off (FTO)
  • Pet Insurance
  • 401 (k) Employer Matching
  • Stock Options
  • Family Planning reimbursement
  • Legal Insurance
  • Identity Theft Protection
  • Child Tutoring Reimbursement
  • Career advancement opportunities
  • Casual Dress
  • On-site gym and dedicated Peloton room at headquarters
  • Company Events (Sports Games, Fitness Competitions, Birthday Celebrations, Contests, Happy Hours)
  • Annual company party
  • Employee Referral Program

Interos is proud to be an Equal Opportunity Employer and will consider all qualified applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity, genetic information, national origin, disability, protected veteran status or any other classification protected by law.

If you are a candidate in need of assistance or an accommodation in the application process, please contact [email protected]

 

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Interos is proud to be an Equal Opportunity Employer and will consider all qualified applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity, genetic information, national origin, disability, protected veteran status or any other classification protected by law.

If you are a candidate in need of assistance or an accommodation in the application process, please contact HR@interos.com

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Ensure Operational Resilience

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Build operational resiliency into your extended supply chain:

  • 889 compliance – ensure market access
  • Data sharing with 3rd parties and beyond – protect reputation
  • Concentration risk – ensure business continuity
  • Cyber breaches – assess potential exposure
  • Unethical labor – avoid reputational harm
  • On-boarding and monitoring suppliers – save time and money