Forward Deployed Engineer
The opportunity:
This is a rare chance to join a company building breakthrough tooling at the intersection of AI research and real-world deployment. The core technology gives teams unprecedented visibility into solving one of the hardest and most consequential problems in modern machine learning. The client roster includes some of the biggest names in AI and life sciences, the team is world-class, and the work you do here will directly shape how industries build trust in the systems they rely on. If you want to be where deep technical ambition meets high-stakes customer impact, this is the role.
Purpose of the role:
Client projects require deep, hands-on involvement. The person in this seat embeds directly within client environments, defining project scope, building data pipelines and integrations, training users on the product, and delivering tailored solutions for the final stretch. They serve as the critical bridge between the internal product team and external stakeholders.
Two paths:
The generalist track calls for a strong builder who can wire up data connections, drive project execution, define deliverables, and keep client timelines on track. Prior experience in embedded client-facing engineering roles at enterprise software companies is a strong signal.
The applied ML track layers deep machine learning expertise on top of solid engineering fundamentals. This person supports model evaluation, fine-tuning workflows, and deep diagnostic work on model behavior. Domain expertise in sciences is a plus, but versatile technical backgrounds work well too.
What good looks like:
Clients realize measurable outcomes from the product, high-complexity projects move from concept to delivery, and the team consistently operates at the cutting edge of the technology.
Non-negotiables:
Genuine engineering ability, this is a building role, not an advisory or architecture one. Strong communication instincts with the confidence to define realistic scope and push back where necessary. Comfort operating in a highly client-facing capacity day to day. For the applied ML path, meaningful depth in machine learning is required.
Preferred but not required:
Background in embedded field engineering at enterprise data or analytics platforms. Domain experience in life sciences or biotech, though backgrounds in hardware automation, robotics, or other technical verticals work as well. Time spent at applied ML infrastructure companies or technically ambitious startups.
Typical client landscape:
Roughly 60% of engagements sit in the life sciences and biotech space, with the remainder spanning hardware automation, robotics, and large-scale AI platform companies. Initial engagements typically run two to four weeks and are designed to convert directly into production deployments.