Today’s List at a Glance
A hand-picked list of top-tier roles for ambitious professionals. Here’s the breakdown:
- 💰 Salary Range: $80K – $220K
- 🏢 Top Companies Hiring: CyberCoders, Ulta Beauty, Posthog
- 📍 Geographic Spread: 5 remote positions (fully distributed roles across the list).
- 🪜 Seniority Level: Primarily senior and lead-level engineering roles, with mid-level research and associate opportunities mixed in.
Featured AI & ML Engineering Roles
REMOTE – Lead AI Engineer at CyberCoders
📍 Location: Remote
💰 Salary: $170K – $220K
Why it’s a great opportunity: High-paying, fully remote Lead AI Engineer role ideal for senior practitioners who want to steer production AI systems with opportunities to embed responsible AI practices across fintech products.
AI Product Engineer at Posthog
📍 Location: Remote
💰 Salary: $100K – $150K
Why it’s a great opportunity: Remote product-focused AI engineering role that combines building end-to-end AI applications with the ability to influence product-level privacy, safety, and responsible-AI design decisions.
Research Engineer – Fundamental AI at Ekman Associates, Inc.
📍 Location: Remote
💰 Salary: $80K – $130K
Why it’s a great opportunity: Remote research role focused on foundational AI — a solid entry point for engineers who want to work on core model behavior and safety research that advances ethical AI capabilities.
Lead AI Engineer at Ulta Beauty, Inc.
📍 Location: Remote
💰 Salary: $119.3K – $170K/yr
Why it’s a great opportunity: A competitively compensated remote Lead AI Engineer opportunity at a large consumer brand where you can influence responsible deployment of AI in customer-facing systems.
Our AI Resume Optimizer can help you tailor your resume’s content, section by section, for each of these specific roles.
AI Engineer (Associate) – Remote at Huron Consulting Group
📍 Location: Remote
💰 Salary: $112K – $147.5K/yr
Why it’s a great opportunity: Mid-level remote AI engineering role with strong compensation and consulting scope — a great fit for engineers looking to integrate responsible AI practices across enterprise client projects.
Strategic Playbook for Landing These Roles
Profile of an Ideal Candidate
- Core Responsibility: Lead the end-to-end development, validation, and responsible deployment of production-grade AI systems and foundational research to improve model behavior and product outcomes.
- Essential Experience: A strong background in machine learning research or engineering, production ML/MLOps experience, and demonstrated work on responsible AI, model safety, or product-facing model delivery.
- Key Competencies: Beyond technical prowess, these roles demand cross-functional leadership, clear stakeholder communication, product thinking, and the ability to translate research into measurable business impact.
The Resume Blueprint: Keywords & Metrics
Keywords to Target:
Responsible AI
MLOps
Model Research
Cross-functional Leadership
Metrics that Matter:
✅ Improved model F1 by 10–15 percentage points — highlight before/after metrics and the business outcome (e.g., increased retention, conversion, or reduced fraud).
✅ Reduced inference latency by 3–4x — quantify latency improvements, cost savings, and effect on user experience or throughput.
✅ Scaled models to serve 100k–1M+ daily requests with 99.9% reliability — call out availability, cost-per-request, and any automation you added to CI/CD or monitoring.
Nailing the Narrative: Your Interview Strategy
Be prepared to answer tough, strategic questions. Here are some specific examples:
“Describe a complex AI research problem you’ve faced that required unconventional thinking. How did you approach it, and what was the ultimate impact of your solution?”
“Walk us through a production incident involving a machine learning model: what caused it, how you diagnosed it, and what long-term fixes you implemented to prevent recurrence?”
“How do you evaluate trade-offs between model performance, latency, and cost when designing a product-facing ML feature? Give a concrete example.”
