Machine Learning / Arlington, VA

Senior Machine Learning Engineer, MLOps

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 a world-class Senior Machine Learning (ML) Ops Engineer to help us design, build, scale, and lead our data and machine learning infrastructure. This role will work closely with dev ops, data science, engineering, and product to ensure we deliver reliable and accurate data on optimal infrastructure to our users who depend on Interos to keep their companies in action.

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, containerized environments, graph databases, and predictive modeling. And you want it all in a high-growth startup.

The selected applicants will have the opportunity to optimize and expand our infrastructure and organization for model generation, orchestration, deployment, health, diagnostics, and metrics.

Our crack team of software engineers and data analysts are ready and waiting with the best models possible for your team to operationalize on AWS infrastructure.

KEY RESPONSIBILITIES

  • Serve as key technical resource for machine learning engineers, helping them transition their ML models into production environments.
  • Serve as a subject matter expert for Machine Learning Ops, 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.
  • Own production machine learning pipelines for in-house developed supply chain risk models used by some of the biggest enterprises in the world.
  • Develop systems, tools, & processes to monitor ML models in production, monitoring drift and performance and initiating retraining and validation as necessary.
  • Develop systems, tools, & processes to govern ML models for compliance, bias, versioning, traceability and auditability.
  • Work closely with ML Engineers to advise on implementation. Be critical in the path to getting our talented research team's ideas to market.
  • Recommend and drive architecture/infrastructure to create actionable, meaningful, and scalable solutions for business problems.
  • Establish scalable, efficient, and automated processes for large scale ML model deployments.
  • Manage, monitor, and troubleshoot machine learning infrastructure.

QUALIFICATIONS

  • BS Degree in Computer Sciences (or related technical degree with industry experience).
  • 6+ years of hands-on industry experience.
  • 2+ years of experience deploying robust machine learning APIs in production environments (ideally cloud-based environments such as GCP or AWS) - from model training and versioning to observability and delivery to consumers.
  • 1+ years of experience with Kubernetes.
  • Experience building auto-scaling ML systems.
  • Experience with machine learning lifecycle platforms (MlFlow / Kubeflow)
  • A passion for creating innovative techniques and making these methods robust and scalable.
  • Strong Python programming skills.
  • Experience with databases and data structuring/warehousing.
  • Exposure to machine learning concepts (feature engineering, text classification, and time series prediction) and frameworks with interest in learning more.
  • 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 deploying machine learning models using cloud-based solutions such as SageMaker or Azure ML.
  • Familiarity with and ability to train people on deploying to a containerized environment.
  • Production experience with serverless platforms such as AWS Lambda or Google Cloud Functions.
  • Contributions to open source projects.
  • 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. Accordingly, as of January 4, 2022, 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 no later than 14 days before January 4, 2022.

BENEFITS

  • Comprehensive Health & Wellness package (Medical, Dental and Vision)
  • 10 Paid Holiday Days Off
  • Flexible Time Off (FTO)
  • 401(k) Employer Matching
  • Stock Options
  • 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