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Senior Machine Learning Engineer

CoinbaseRemote — India Posted 5mo ago

Workplace
Remote
Type
Full-time
Level
Senior
Salary
Not listed

Ready to do the most impactful work of your career? At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” Learn more about working at Coinbase.

The CX Intelligence team is part of Coinbase’s Enterprise Applications and Architecture org and builds the customer-facing and internal CX experiences that connect the Help Center, chatbots (CBCB), and agent workflows. The team owns the multi-agent platform which powers coinbase chat, Help Center and agent tooling surfaces, partnering closely with Conversation Design, CX, and other Engg teams to deliver secure, compliant, and scalable AI-powered support. Our work provides the trusted and accurate automated and agent-assisted experiences—helping customers get answers faster while enabling human agents to resolve cases more effectively.

We are hiring an IC5 Machine Learning Engineer to drive the evolution of our conversational ecosystem by building a seamless hybrid vendor-internal chatbot experience. You will lead the design and implementation of a sophisticated unified orchestration layer that coordinates interactions between vendor AI, internal multi-agent systems, and human participants. This role is pivotal in managing complex state transitions, context sharing, and intent routing across a distributed environment. You’ll thrive here if you enjoy high ownership, architecting complex hand-off logic between disparate LLM frameworks, and building reliable AI-enabled products that are fast, measurable, and scalable.

What you'll do:

  • Architect and deploy the orchestration layer that manages state transitions, context sharing, and intent routing across vendor and internal LLM frameworks in a distributed conversational environment.
  • Build production-grade Python services that bridge advanced ML/AI research with reliable, measurable customer-facing products.
  • Lead end-to-end project execution for complex ML initiatives, managing priorities, technical trade-offs, and cross-functional dependencies from design through delivery.
  • Establish best practices for system design, coding standards, and AI/ML development workflows across the team.
  • Mentor engineers on architectural integrity and modern AI/ML patterns, raising the technical bar for the broader team.
  • Conduct design reviews to ensure every feature meets Coinbase's standards for security, scalability, and performance.

Required Skills and Experience:

  • 5+ years of professional experience in machine learning and software engineering, with a track record of shipping production-grade ML services at scale.
  • Hands-on expertise building with modern AI architectures (LLMs, deep learning) and the generative AI ecosystem, including frameworks such as LangGraph, LangSmith, Google ADK, Vertex AI, or AWS Bedrock.
  • Deep proficiency in Python with demonstrated ability to write clean, maintainable, highly-tested production code.
  • Specialized knowledge in at least one domain: NLP, information retrieval, computer vision, or advanced statistical modeling.
  • Proven ability to write technical design documents and present ML system architectures to cross-functional stakeholders, translating complex technical concepts for non-technical audiences.
  • Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.
  • Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human‑in‑the‑loop practices to deliver business‑ready outputs and drive measurable improvements in efficiency, cost, and quality.

Pay Transparency Notice: The target annual base salary for this position can range as detailed below. Total compensation mayalso include equity and bonus eligibility and benefits (including medical, dental, and vision).

Annual base salary range (excluding equity and bonus):₹6,612,600—₹6,612,600 INR
  • Application Limit: Candidates may submit a maximum of 3 applications within a 6-month period.
  • Equal Opportunity Employer: Coinbase is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or genetic information. Applicants with criminal histories will be considered consistent with applicable federal, state, and local laws.
  • US Applicants: View Employee Rights, Know Your Rights, and E-Verify Notice of Participation.
  • Accommodations: If you are an individual with a disability who needs a reasonable accommodation, email us your request and contact info at accommodations[at]coinbase.com. Need screen reading technology? Click here to download a free compatible screen reader and view thetutorial.
  • Data Privacy & Arbitration: By submitting your application, you agree to our Candidate Privacy Notice. US applicants: By submitting your application, you agree to Arbitration of Disputes.

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Salary benchmark

Data & AI roles · 81 posted USD salaries

25th: $246kMedian $302k75th: $396k

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Skills they ask for most: LLM / GenAI, SQL, Python, Web3 / Blockchain.

How to stand out for this role

  • Quantify model or analysis impact in business terms (conversion, cost, accuracy lift), not just metrics like AUC.
  • Show the full pipeline you owned: data sourcing, cleaning, modelling, deployment and monitoring.
  • Have a short notebook or write-up ready that explains a trade-off you made and why.
  • List the specific warehouse and orchestration tools you’ve used (e.g. Snowflake, dbt, Airflow).
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