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Principal Solutions Architect

Neptune.ai

Principal Solutions Architect

Neptune.ai

USA

·

On-site

·

Contract

·

1w ago

Benefits & Perks

Remote Work

Flexible Hours

Equity

Unlimited PTO

Remote Work

Flexible Hours

Equity

Unlimited Pto

Required Skills

Data Science

Machine Learning

AI

PyTorch

TensorFlow

JAX

AWS

GCP

Azure

As a Principal Solutions Architect, you will be engaging with top research labs in the world, helping them maximize the value of our platform.

You will lead Proof of Concepts (Po Cs) for potential high-value customers, conduct technical demos, and provide advanced troubleshooting, helping drive product adoption and identifying upsell opportunities. This is a hands-on, high-impact role focused on driving customer success through expert technical support and collaboration.

Key Responsibilities:

  • Technical Leadership in Customer Engagements: Serve as the primary technical advisor in high-priority customer engagements, understanding customer needs and driving solutions through on-site visits (typically once a month) to key locations, including San Francisco, Toronto, and Tel Aviv.

  • Bridge Between Customers and Product Team: Capture and translate customer insights into detailed product requirements for engineering and UX teams, ensuring solutions align with real-world needs.

  • Drive Proof of Concepts (Po Cs): Lead and execute Po Cs for prospective customers, ensuring smooth integration, technical validation, and a clear path to adoption.

  • Technical Demos & Discussions: Conduct detailed technical demos and discussions, demonstrating how Neptune can solve specific customer challenges and integrate seamlessly with their workflows.

  • Upsell & Expansion Opportunities: Identify potential upsell and cross-sell opportunities by aligning product capabilities with evolving customer requirements.

  • Problem Solving & Escalation Support: Act as a high-level technical troubleshooter for customer issues, collaborating with internal teams to resolve complex technical challenges.

  • Documentation and Knowledge Sharing: Contribute insights on customer use cases, best practices, and focus areas for case studies, helping to develop resources that address common customer adoption challenges.

You might be a fit if you have:

  • Master’s or PhD in Mathematics, Physics, Data Science, or a related quantitative field.

  • 5-10 years of hands-on experience in data science, machine learning, or AI, with expertise in model development, statistical analysis, and data pipelines.

  • Strong proficiency in AI/ML frameworks (e.g., Py Torch, Tensor Flow, JAX) and a proven ability to apply these frameworks to real-world customer needs.

  • Experience with cloud platforms (AWS, GCP, or Azure).

  • Willingness to travel up to 50%, primarily to the USA, including potential two-week stays each month, to lead customer engagements and ensure successful product integration.

  • Proven track record in customer-facing roles, with experience leading Po Cs and delivering technical solutions that meet customer goals.

  • Strategic insight into how technical decisions impact business outcomes, with a focus on identifying upsell opportunities.

  • High degree of autonomy, ownership, and problem-solving skills in fast-paced environments.

About Neptune:

Neptune Scale empowers AI research teams to develop foundation models with efficiency, scale, and precision. As large-scale model training grows more complex, requiring weeks or even months-long processes, millions of parameters, and high compute resources, Neptune provides a scalable experiment tracking platform to address these operational challenges.

The platform’s architecture supports the ingestion of millions of data points per second, fast rendering of massive tables and charts, as well as filtering and comparison of thousands of metrics in almost real time.

Built for collaboration, Neptune's customizable reports, dashboards, and secure self-hosted deployment make it easy for teams building foundation models to share insights and work efficiently.

Neptune is the experiment tracker built to accelerate AI innovation, designed to match the scale and speed demanded by today’s most ambitious AI research teams.

We offer:

  • Flexibility: 100% remote work with flexible working hours.

  • Contract: Cooperation on an ICA basis only.

  • Share in our success: Participate in the Employee Stock Option Plan and be part of our growth journey;

  • Time off: 20 paid service-free days per year;

  • Ownership and impact: Space to take action, bring your ideas to life, and make a real impact.

Any questions?

Check our ultimate guide for candidates to neptune.ai.
Don’t hesitate to contact our Talent Acquisition team, and take a look at our About us page to get to know the story and faces behind Neptune.

By applying, you consent for neptune.ai to process your personal data to assess your suitability for the role you have applied for in accordance with the General Data Protection Regulation (GDPR). Your personal data will remain confidential and shared only with authorized personnel involved in the recruitment process. You have the right to access, rectify, or delete your personal data at anytime.
With your optional consent, we can retain your data for up to 12 months after the application to consider you for future suitable roles if you’re not a match for the current position.

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About Neptune.ai

Neptune.ai

Neptune.ai

Series A

Neptune.ai provides a metadata store and experiment management platform for machine learning teams to track, organize, and collaborate on ML experiments and model development.

51-200

Employees

San Francisco

Headquarters

Reviews

4.2

15 reviews

Work Life Balance

3.5

Compensation

4.9

Culture

4.4

Career

4.8

Management

3.8

91%

Recommend to a Friend

Pros

Working on cutting-edge AI/ML technologies

Top-tier compensation with excellent equity

Strong research and publication culture

Cons

Competition for resources and recognition

Ambiguity in rapidly evolving field

Work-life balance can suffer during critical periods

Interview Experience

36 interviews

Difficulty

4.0

/ 5

Duration

21-35 weeks

Offer Rate

23%

Experience

Positive 65%

Neutral 20%

Negative 15%

Interview Process

1

Recruiter Screen

2

ML Coding

3

ML System Design

4

Research Discussion

5

Team Interviews

Common Questions

ML fundamentals

Design an ML system

Research paper discussion

Statistical concepts