BJAK Logo

BJAK

Senior Machine Learning Engineer

Reposted 4 Days Ago
Remote or Hybrid
Hiring Remotely in United States
Senior level
Remote or Hybrid
Hiring Remotely in United States
Senior level
The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.
The summary above was generated by AI
Company

A1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior.

 
Role

As a Senior Member of Technical Staff, Machine Learning, you are an independent owner of critical ML subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale.

This is a hands-on, high-impact role focused on depth.

Focus
  • Build core ML systems that power a proactive, long-horizon AI product.

  • Own work end-to-end: data preparation, training, evaluation, inference, and iteration.

  • Turn research ideas into working systems that run reliably in production.

  • Debug model failures and system issues using real production signals.

  • Iterate quickly: ship, measure outcomes, refine, and repeat.

  • Collaborate closely with research, product, and engineering to deliver real user impact.

  • Mentor and review work from other ML engineers through example and technical judgment.

  • Work under real production constraints: latency, cost, reliability, and safety

Tech Stack
  • Python

  • PyTorch / JAX

  • GPU-based training and inference systems

Ideal Experience
  • You have built and shipped ML systems used by real users.

  • You understand how modern ML models behave — and misbehave — in production.

  • You write strong, production-quality code and think in systems, not scripts.

  • You take ownership, work independently, and push work across the finish line.

  • You learn fast, communicate clearly, and improve through iteration.

Outcomes
  • ML models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets.

  • Complex production issues are monitored, debugged, and resolved with minimal disruption.

  • Training, inference, and data pipelines are robust, scalable, and maintainable over time.

  • Drives measurable improvements in ML systems based on real-world signals and user feedback.

  • Provides mentorship and technical guidance to peers, raising the overall ML engineering standard.

  • Collaborates cross-functionally to ensure ML features integrate seamlessly into products and meet business goals.

How We Work

The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product

Interview process

If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.

Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.

We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.

Similar Jobs

Yesterday
Easy Apply
Remote
USA
Easy Apply
Senior level
Senior level
Artificial Intelligence • Blockchain • Fintech • Financial Services • Cryptocurrency • NFT • Web3
Design and build machine learning systems for Coinbase, responsibly use generative AI tools and copilots, apply human-in-the-loop practices, and deliver measurable efficiency, cost, and quality improvements while collaborating in a remote-first environment with periodic in-person surges.
Top Skills: GeminiGenerative AiGleanLibrechat
7 Days Ago
In-Office or Remote
195K-343K Annually
Senior level
195K-343K Annually
Senior level
Blockchain • eCommerce • Fintech • Payments • Software • Financial Services • Cryptocurrency
As a Senior Machine Learning Engineer, you will lead model validation for AI systems, challenge model soundness, and build validation tools for high-stakes areas such as credit and fraud prevention.
Top Skills: AWSCiDatabricksGCPGcp Vertex AiGitJIRALightgbmLinearMlflowNumpyPandasPrefectPythonPyTorchScikit-LearnSnowflakeXgboost
13 Days Ago
Remote
United States
175K-230K Annually
Senior level
175K-230K Annually
Senior level
Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Software • Generative AI
The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.
Top Skills: AWSC++KubernetesPythonPyTorchScikit-LearnSparkTensorFlow

What you need to know about the Colorado Tech Scene

With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.

Key Facts About Colorado Tech

  • Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
  • Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
  • Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
  • Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
  • Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account