Job VC
Lead Computer Vision/ML Engineer
Technologies
Description
About Us
Our client is
MilTech startup
building an
on-premises platform for automated annotation of UAV video and training computer vision models for autonomous flight
.
We have a
strategic partnership with one of the largest Ukrainian UAV manufacturers
and access to what is likely
the largest privately owned UAV video dataset in the world
.
The company is at a very early stage —
the funding is secured, the technical vision exists, and the data is available
. Our focus now is building the core engineering team to turn this platform into reality.
The Role
We are looking for a
Lead Computer Vision / Machine Learning Engineer
to join as a
founding member of the engineering team
.
This role is highly hands-on and strategic. You will
define the ML architecture, select the tools and frameworks, and build the machine learning pipeline from the ground up
. As the platform grows, you will also
build and lead a team of ML engineers
.
While the high-level technical direction is defined — including
petabyte-scale video processing, active learning pipelines, and edge deployment on NVIDIA Jetson devices
— the implementation details will be driven by your expertise and technical judgement.
Responsibilities ML Architecture & Technical Direction
Design and implement the
end-to-end ML pipeline
, from raw video ingestion to model deployment on UAV hardware
Evaluate and select appropriate
frameworks and infrastructure tools
Build systems for
experiment tracking, model versioning, and reproducible training
Establish the
training infrastructure and ML development workflow
Make strategic
build vs. integrate decisions
across the ML stack
Data Pipeline & Curation
Design
video preprocessing pipelines
including:
clip segmentation
frame extraction
scene detection
embedding generation
Develop
data curation workflows
to extract high-quality training datasets from large-scale raw video
Define the
annotation ontology and labeling standards
for detection, classification, segmentation, and multi-object tracking
Model Development
Train and improve
computer vision models
for:
object detection
scene classification
object tracking
segmentation
Implement
distributed multi-GPU training
for large-scale experiments
Design and implement
active learning pipelines
including:
automated labeling
confidence-based routing
human review workflows
retraining triggers
Define
model evaluation metrics and promotion criteria
Edge Deployment
Optimize models for
real-time inference on NVIDIA Jetson hardware
Implement model optimization techniques including:
ONNX conversion
TensorRT acceleration
INT8 quantization
Design the
model delivery pipeline
from training registry to deployed UAV systems
Team Building
Hire and mentor
ML engineers as the team grows
Establish engineering practices including:
code review
testing standards
documentation
Own and communicate the
ML technical roadmap
RequirementsMust Have
5+ years of experience in computer vision and deep learning
, including production deployments
Strong proficiency with
PyTorch
, including distributed training
Experience building
object detection and tracking pipelines
(YOLO, RT-DETR, Detectron2, or similar)
Experience optimizing models for
edge or mobile deployment
(TensorRT, ONNX, SNPE, or similar)
Experience building
ML infrastructure and training pipelines
Strong Python skills and ability to write
production-quality code
Experience
leading or mentoring engineers
Nice to Have
Experience with
UAV, drone, robotics, or autonomous systems
Experience deploying models on
NVIDIA Jetson
Experience with
high-performance video processing pipelines
(DeepStream or custom)
Experience in
defense or MilTech environments
Experience defining
ML architecture from scratch in early-stage startups
C++
experience for performance-critical components
What We Offer
Founding engineering role
with direct influence on company technology
Ownership of the
entire ML pipeline
from raw data to deployed models
Access to
one of the largest privately owned UAV video datasets in the world
Dedicated
on-prem GPU infrastructure
Open-source-first engineering approach
Opportunity to
build and lead a machine learning team
Competitive salary
Stock options
Our client is
MilTech startup
building an
on-premises platform for automated annotation of UAV video and training computer vision models for autonomous flight
.
We have a
strategic partnership with one of the largest Ukrainian UAV manufacturers
and access to what is likely
the largest privately owned UAV video dataset in the world
.
The company is at a very early stage —
the funding is secured, the technical vision exists, and the data is available
. Our focus now is building the core engineering team to turn this platform into reality.
The Role
We are looking for a
Lead Computer Vision / Machine Learning Engineer
to join as a
founding member of the engineering team
.
This role is highly hands-on and strategic. You will
define the ML architecture, select the tools and frameworks, and build the machine learning pipeline from the ground up
. As the platform grows, you will also
build and lead a team of ML engineers
.
While the high-level technical direction is defined — including
petabyte-scale video processing, active learning pipelines, and edge deployment on NVIDIA Jetson devices
— the implementation details will be driven by your expertise and technical judgement.
Responsibilities ML Architecture & Technical Direction
Design and implement the
end-to-end ML pipeline
, from raw video ingestion to model deployment on UAV hardware
Evaluate and select appropriate
frameworks and infrastructure tools
Build systems for
experiment tracking, model versioning, and reproducible training
Establish the
training infrastructure and ML development workflow
Make strategic
build vs. integrate decisions
across the ML stack
Data Pipeline & Curation
Design
video preprocessing pipelines
including:
clip segmentation
frame extraction
scene detection
embedding generation
Develop
data curation workflows
to extract high-quality training datasets from large-scale raw video
Define the
annotation ontology and labeling standards
for detection, classification, segmentation, and multi-object tracking
Model Development
Train and improve
computer vision models
for:
object detection
scene classification
object tracking
segmentation
Implement
distributed multi-GPU training
for large-scale experiments
Design and implement
active learning pipelines
including:
automated labeling
confidence-based routing
human review workflows
retraining triggers
Define
model evaluation metrics and promotion criteria
Edge Deployment
Optimize models for
real-time inference on NVIDIA Jetson hardware
Implement model optimization techniques including:
ONNX conversion
TensorRT acceleration
INT8 quantization
Design the
model delivery pipeline
from training registry to deployed UAV systems
Team Building
Hire and mentor
ML engineers as the team grows
Establish engineering practices including:
code review
testing standards
documentation
Own and communicate the
ML technical roadmap
RequirementsMust Have
5+ years of experience in computer vision and deep learning
, including production deployments
Strong proficiency with
PyTorch
, including distributed training
Experience building
object detection and tracking pipelines
(YOLO, RT-DETR, Detectron2, or similar)
Experience optimizing models for
edge or mobile deployment
(TensorRT, ONNX, SNPE, or similar)
Experience building
ML infrastructure and training pipelines
Strong Python skills and ability to write
production-quality code
Experience
leading or mentoring engineers
Nice to Have
Experience with
UAV, drone, robotics, or autonomous systems
Experience deploying models on
NVIDIA Jetson
Experience with
high-performance video processing pipelines
(DeepStream or custom)
Experience in
defense or MilTech environments
Experience defining
ML architecture from scratch in early-stage startups
C++
experience for performance-critical components
What We Offer
Founding engineering role
with direct influence on company technology
Ownership of the
entire ML pipeline
from raw data to deployed models
Access to
one of the largest privately owned UAV video datasets in the world
Dedicated
on-prem GPU infrastructure
Open-source-first engineering approach
Opportunity to
build and lead a machine learning team
Competitive salary
Stock options