Machine Learning Engineer (Applied ML)

About Us

Most AI is frozen in place - it doesn't adapt to the world. We think that’s backwards. Our mandate is to build efficient intelligence that evolves in real‑time. Our vision is AI systems that are flexible, personalized, and accessible to everyone. We believe efficiency is what makes this possible — it’s how we expand access and ensure innovation benefits the many, not the few. We believe in talent density: bringing together the best and most driven individuals to push the boundaries of continual adaptation. We’re looking for builders and creative thinkers ready to shape the next era of intelligence.

Our research principles

Sweat the details. Technical excellence requires obsessing over every detail. We co‑design serving, algorithms, and interface as one system to maximize efficiency and enable real‑time adaptation.

Product as proof. Research ideas are tested through working products. If it doesn't improve what users can do, we question whether it matters. We share what moves the field; we don't optimize for paper count.

Move with conviction. We operate with urgency as a unified team, concentrating resources on a few high‑conviction bets where research, product, and impact intersect.

The Role

We are obsessed with efficiency—allowing for real‑time evolution of AI depends on making adaptable data and algorithms extremely efficient. The ideal candidate brings an expertise in AI/ML, a strong track record of delivering impact, and cares about making impact in the real world. Act as a trusted technical advisor to strategic customers—translating needs into actionable plans.

We’re looking for a Machine Learning Engineer who thrives at the intersection of applied research and building real‑world products. You’ll drive the design and implementation of adaptable data strategies. You’ll work directly with the founding team, contributing to both the research direction and the product vision. This role demands technical excellence and a passion for delivering real‑world impact.

Responsibilities

• Deliver Real-World Impact: Lead the development and deployment of efficient, adaptive ML systems in real production environments.
• Customer Collaboration: Act as a trusted technical advisor to strategic partners—turning complex needs into actionable solutions.
• Hands‑on Impact: The ideal candidate will own implementation of data products at Adaptable Labs. This position requires creativity and technical execution in addressing novel challenges related to data, interaction, and evaluation.

Qualifications

• Bachelor’s degree in Computer Science, Machine Learning, or a related field
• Good communicator, excited about aligning technical execution with business goals.
• 3-4 years of experience in machine learning, applied research, or systems-level engineering for AI. Bachelor’s degree (or higher) in Computer Science, Machine Learning, or a related field.
• Strong software engineering skills and familiarity with ML frameworks (e.g., PyTorch, JAX, TensorFlow).
• Excellent communication skills and ability to align technical work with high-level goals.
• A mindset of ownership, curiosity, and a bias toward action.
• Bonus: experience with online learning, reinforcement learning, or efficient ML architectures.
• Bonus: track record of deploying ML systems that solved real business problems.

Above all, we’re looking for great teammates who make work feel lighter and aren’t afraid to go out on a limb with bold ideas. You don't need to be perfect, but you do need to be adaptable. We encourage you to apply, even if you don’t check every box.

Benefits

• Flexible work: In‑person collaboration in the Bay Area, a distributed global‑first team, and quarterly offsites.
• Adaption Passport: Annual travel stipend to explore a country you've never visited. We’re building intelligence that evolves alongside you, so we encourage you to keep expanding your horizons.
• Lunch Stipend: Weekly meal allowance for take‑out or grocery delivery.
• Well‑Being: Comprehensive medical benefits and generous paid time off.