dannycastonguay

He knew :)

Also looking for: Junior AI Engineer / Data Scientist | Australia (Remote or Melbourne) | Full-time

Early-career role building + shipping real production ML. 1–3 yrs ML/Python/data pipelines. Cloud exp. (AWS/GCP/Azure) a plus. Remote-friendly across East Aus. Salary 100–115K AUD. Work closely with Singapore lead; real impact + fast learning curve.

__

Senior Data Scientist | USA (Remote) | Full-time | 150K USD + commission

Lead applied AI delivery + enterprise client conversations. 6–10 yrs in ML/DS/AI product work; MLOps + strategy skills. Consulting or enterprise background preferred. Master’s/PhD optional. Looking for polished stakeholder presence (ex-FAANG/MBB vibes welcome). ___

bld.ai | Junior AI Engineer / Data Scientist | USA (Remote) | Full-time | 100K USD + commission

1–3 yrs ML engineering or DS, Python + pipelines, cloud exposure. Comfortable async + independent; startup pace. Growth path to technical lead.

[email protected]

bld.ai | Senior Data Scientist | Singapore | Hybrid (non-remote) | Relocation OK | Full-time

Lead client-facing AI + data science projects for enterprise clients. 6–10 yrs applied ML/AI experience, strong MLOps + data strategy background. Consulting or enterprise experience required. Salary 125–140K SGD. Master’s/PhD nice but not required.

Email [email protected]

Full-Stack / App Developer (Client-Facing) Location: USA (Houston, Chicago, or Bay Area, CA) Compensation: 110–125K USD + commission

Role Summary: We’re looking for an application developer who thrives at the intersection of software engineering and client delivery. You’ll design, build, and deploy production-grade applications—often integrating AI or data-driven components—and work closely with enterprise clients on-site (~50% travel). You’ll collaborate with our AI and data science teams to bring robust, user-ready systems to life.

Requirements: 1. 4–7 years of experience in full-stack or application development. 2. Strong proficiency in Python, JavaScript/TypeScript, and modern frameworks (e.g., React, FastAPI, Node.js, or Flask). 3. Experience deploying to cloud environments (AWS, GCP, or Azure). 4. Ability to translate business requirements into maintainable, scalable code. 5. Excellent client-facing communication and collaboration skills. 6. Willingness to travel up to 50% for on-site work with U.S. clients.

Ideal Profile: A hands-on builder with a consulting mindset—comfortable both coding independently and representing the team in client environments. Prior experience in AI product development, data-intensive systems, or enterprise integrations is a plus.

Junior AI Engineer / Data Scientist Location: USA broadly 100K USD + commission Role Summary: Support the development and deployment of AI systems, focusing on experimentation, data processing, and performance optimization. You’ll work closely with the Singapore lead to execute model training, testing, and integration tasks in production environments.

Requirements: 1. 1–3 years experience in machine learning, Python, and data pipelines. 2. Exposure to cloud environments (AWS, GCP, or Azure). 3. Comfortable working independently and asynchronously with remote teams. 4. Good communication skills and disciplined execution. 5. Bachelor’s in Computer Science, Data Science, or related field.

Ideal Profile: Early-career data scientist or ML engineer with a startup or research background; eager to learn, ship fast, and grow into a lead role.

Senior Data Scientist Location: USA broadly 150K USD + commission Role Summary: Lead client-facing AI engagements, represent the firm in discussions with enterprise clients, and oversee technical delivery across applied AI and data science projects. You’ll translate business problems into practical AI solutions while guiding a small, distributed engineering team.

Requirements: 1. 6–10 years in applied machine learning, data science, or AI product development. 2. Proven experience with enterprise clients or consulting environments. 3. Strong communication and presentation skills. 4. Technical depth in model design, MLOps, and data strategy. 5. Master’s or PhD preferred but not required.

Ideal Profile: Ex-FAANG, Ex-MBB, or similar background; confident, articulate, credible in front of senior stakeholders.

Why do antitrust proposals always suggest breaking up Google by function—Search, Ads, Gmail, etc.? What if, instead, we cloned the whole company?

Start by splitting Google into two identical, full-stack companies, each with all the core products. A year later, split them again. Over time, you get 4 or 8 Googles competing across the board.

Employees could be assigned algorithmically to avoid chaos. This feels more like cell division than amputation—preserving the synergies while creating competition.

Agreed. It's important to put enough effort that you find meaning in your work, but not so much that it ruins your wellbeing. Here's a rough algorithm that works for me:

1. Estimate the hours you think it will take to complete a task.

2. Double it and let the team know you did that.

3. Do the work well including good documentation.

4. Assess your progress when you've spent 50% of the planned hours. If you're not at least halfway done, avoid overworking. Instead, seek help within the team and descope.

5. Utilise any extra time for learning new and useful skills, if you finish ahead of schedule.

Cheers