We're looking for an Applied ML Engineer who thrives at the intersection of applied research and building real-world products, and who's equally comfortable sitting across from a customer as they are in the codebase.
You'll work directly with strategic customers tackling some of their most consequential problems — figuring out where our platform falls short, and building the ML solutions that close the gap. Then you'll do it again for the next customer with an entirely different domain, data landscape, and set of constraints. The problems you solve in the field become the product.
ResponsibilitiesFind and Unlock Alpha: Go deep on customer problems and data workflows to identify the highest-leverage opportunities others miss — then build the solutions that capture them.
Ship What's Missing: When the product doesn't cover it, you do. Identify gaps through hands-on customer engagement and implement production-grade ML solutions that fill them.
Drive Adaptable Data Strategy: Lead the design and implementation of efficient, adaptive ML systems across real production environments and varied customer tech stacks.
Own the Outcome: You're not handing off a deck — you're a strategic and technical partner from discovery through deployment, accountable for results.
Raise the Bar: Develop compelling demos, deliver technical presentations to senior stakeholders, and set the standard for what great looks like across our customer base.
4+ years of experience in machine learning, applied research, or systems-level engineering for AI; 2+ years in a customer-facing technical role
Strong software engineering skills and familiarity with ML frameworks (e.g., PyTorch, JAX, TensorFlow)
Solid understanding of data modeling for training and how curation decisions shape model performance
Excellent communication skills and the ability to translate complex technical work into business impact across varied stakeholder environments
A mindset of ownership, curiosity, and a bias toward action
Bonus: Experience with online learning, reinforcement learning, or efficient ML architectures
Bonus: Experience training or fine-tuning models using human feedback, reward signals, or other adaptive learning techniques
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.
About UsMost 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.
Flexible work: In-person collaboration in the Bay Area, a distributed global-first team, and team 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.
Skills Required
- 4+ years of experience in machine learning, applied research, or systems-level engineering for AI
- 2+ years in a customer-facing technical role
- Strong software engineering skills
- Familiarity with ML frameworks (PyTorch, JAX, TensorFlow)
- Solid understanding of data modeling for training and how curation decisions shape model performance
- Excellent communication skills and ability to translate technical work into business impact
- Mindset of ownership, curiosity, and a bias toward action
- Experience with online learning, reinforcement learning, or efficient ML architectures
- Experience training or fine-tuning models using human feedback, reward signals, or other adaptive learning techniques
What We Do
Adaption builds AI systems that adapt as real-world conditions change, evolving through real-world interaction. Their vision is to create flexible, personalized, and accessible AI systems that are efficient and continually adapt.
