Engineering Manager, ML (Washington DC or Remote)

| Washington DC +1 more | Remote
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Truebill is a YC-backed startup with offices in San Francisco and Washington, D.C. Our DC office is right on the Silver Spring metro! Hundreds of thousands of people use Truebill to manage their daily finances and take control of their money.

With a mission to improve the financial health of everyday people, Truebill is transforming the way people manage their expenses and grow their net worth. Through helping people cancel unnecessary subscriptions, negotiating bills, and securing refunds, we save our members money while helping them regain control over their finances.

Truebill relies on machine learning in a number of critical path systems including transaction classification, customer lifetime value estimation, and cash flow prediction. We've reached the next stage of our growth in both complexity and team size where a manager is needed. This is a hybrid managerial role that is a mix of technical and people leadership where you'll get a chance to write code and know the inner operating details of the system, while also building out the operating system to enable the team to scale. You'll collaborate with colleagues on the data, engineering, product, and growth teams to ship scalable, reliable, and observable systems that the entire company can build upon and leverage. 


In this role, you will:

  • Lead technical machine learning projects and manage their delivery to end-users (product, growth, engineering, data)
  • Manage, mentor, and grow a team of machine learning engineers, researchers, and analysts who build our machine learning infrastructure and systems
  • Design and lead the construction of robust machine learning patterns and systems that enable Truebill to scale to millions (billions?!) of predictions per day
  • Oversee the creation, curation, and availability of human and machine labeled data to train our models in
  • Lead the team in the implementation of measurement best practices for their particular model topology
  • Design observability, monitoring, and alerting on the inference system such that we can sleep at night while our machines chug away
  • Oversee the construction and deployment across multiple classes of ML problems (time series, classification, NLP extractions, and numerical predictions, LTV)


  • You have demonstrated practical experience running multiple ML projects in a production environment that are critical paths where inference time and reliability are key operating metrics
  • Ferocious documenter (we're an async organization that works remotely and documentation is essential to our success). Bonus points if you fix Notion search someday
  • Experience in the curation and construction of a training data corpus, particularly ones susceptible to drift in both time and composition due to human behavior
  • You're a player-coach - sometimes it just makes sense to write the code yourself and show the way forward or take on some extra capacity for the team
  • Have experience with state-of-the-art models and compute technologies (transformer models, neural networks, SageMaker, Vertex AI) cloud based ML systems
  • Comfortable with a scripting language and building an end-to-end pipeline. Understand in-depth the construction of pre-processing, training, modeling versioning, deployment, and monitoring systems
  • Have managed a team of machine learning engineers and scientists to solve business problems at large volume inference problems at consumer or b2b scale (think transactions, trading, agriculture)
  • A background in product experimentation and measurement is highly desired, as most models are champion/challenger tested with consumer KPIs (LTV, Churn, Activation) as primary decision points
  • Bonus points for experience with low latency streaming solutions on modern cloud workflow pipelines


  • Health, Dental & Vision Plans
  • Competitive Pay
  • Matching 401k
  • Unlimited PTO
  • Lunch daily
  • Snacks, Kombucha & Coffee on tap
  • Commuter benefits

Truebill, Inc. is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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