Manager, Applied Research

Quidient•Columbia, MD

21h•Hybrid

About The Position

Quidient is a deep tech AI company pioneering advancements in Generalized (5D) Scene Reconstruction (GSR). GSR is poised to become one of the world’s great digital product categories (think GPS, MRI, and LMM). Our flagship GSR product, Quidient Reality®, is a powerful API that enables anyone with a mobile device to virtualize, visualize, and measure anything. Words relevant to Quidient include Generative AI, Physics-Informed AI, Large Scene Models (LSMs), Large World Models (LWMs), and API-First. Overview Quidient is seeking a Manager of ML & Reconstruction to lead applied machine learning improvements across our Generalized Scene Reconstruction Platform. You will own the ML engineering function within our Product Development group, driving quality improvements in our 3D scene reconstruction pipeline, while building and managing a high-performing team of engineers and applied scientists. This is a leadership-first role. You will manage cross-functional workstreams across the Applications & API, Platform, and Advanced Product Development teams. You will translate technical tradeoffs into delivery decisions and contribute meaningfully to the codebase where your domain expertise drives the most value. You will ship production ML into a system already in customers’ hands. This position is full-time, hybrid, and reports to Quidient’s Director of Software Engineering.

Requirements

Master’s degree in Computer Science, Electrical Engineering, or a related field; PhD preferred. Bachelor’s with exceptional experience considered.
7+ years of experience in ML engineering or applied research, with at least 5+ years in a people management role overseeing engineers and/or applied scientists.
Strong production ML expertise: model selection, deployment, inference optimization, and operational ownership.
Experience spans traditional ML and applied deep learning; Not a LLM-focused role.
Proven ability to review, direct, and contribute to a production codebase.
Experience driving cross-functional alignment across engineering, research, and product stakeholders in an ambiguous, fast-moving environment.
Heavy experience in 3D reconstruction, computer vision, or spatial computing domains (e.g., NeRF, 3D Gaussian Splatting, SLAM, SfM, or related techniques).
Willingness to work in a hybrid capacity from our Technology Center in Columbia, Maryland, or willing to relocate.
This position is not eligible for H-1B sponsorship.
Meet Quidient, customer, and government security requirements, which may include, but are not limited to a background check, citizenship verification, and Criminal Justice Information Services verification.

Nice To Haves

Background in light transport, physically-based rendering, or GPU-accelerated compute.
Experience with pose estimation, SLAM, or multi-view reconstruction
Track record of shipping ML in production in a latency-sensitive or hardware-constrained environment.
Prior experience at a deep tech startup or in a defense/government-adjacent technology context.
Scientist or applied researcher who has successfully transitioned into an engineering leadership role.

Responsibilities

Drive ML-driven quality improvements across the production 3D scene reconstruction pipeline, including panel geometry, background reconstruction, and output fidelity.
Own model selection, deployment, inference optimization, and operational ownership of ML systems in production.
Evaluate and apply traditional ML and applied deep learning techniques to improve reconstruction quality; understand when each approach is appropriate.
Review and contribute to the production codebase, evaluate tradeoffs, identify bottlenecks, and guide technical decisions.
Build, manage, and grow a team of ML engineers and applied scientists within the Advanced Product Development team.
Drive cross-functional alignment across the Applications & API and Platform teams.
Partner with the APD technical leads and Director of Software Engineering to scope work, resolve technical blockers, and maintain delivery velocity.
Operate effectively in a fast-moving startup where priorities shift; maintain focus on the highest-impact work without losing team momentum.