Machine Learning Engineer; Applied ML

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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 expertise in AI/ML, a strong track record of delivering impact, and a passion for making real-world impact. 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, working 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.

• Lead the development and deployment of efficient, adaptive ML systems in real production environments.
• Act as a trusted technical advisor to strategic partners, turning complex needs into actionable solutions.
• Own implementation of data products at Adaptable Labs, creatively addressing novel challenges related to data, interaction, and evaluation.

• 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.
• Strong software engineering skills and familiarity with ML frameworks (e.g., PyTorch, JAX, Tensor Flow).
• Excellent communication skills and ability to align technical work with high‑level goals.
• 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.

• Be part of the founding team, shaping both the research agenda and the product direction.
• Work at the cutting edge of AI efficiency research, where constraints drive creativity.
• Collaborate with world‑class peers across ML algorithms and hardware acceleration.
• Contribute to building a company where efficiency isn’t an afterthought — it’s the core principle.
• Competitive salary + meaningful equity.
• Budget for learning, development, and certifications.