Sr. Machine Learning Engineer, Autonomy

Heven AeroTech•Bingen, WA

4d•$150,000 - $172,000•Onsite

About The Position

Reporting to the Head of Mission Systems and Software, the Senior Machine Learning Engineer, Autonomy owns the technical development and delivery of machine learning and autonomy capabilities for Heven AeroTech uncrewed aircraft systems. The role requires an experienced engineer who can turn mission and operational needs into software requirements, architecture, implementation, and demonstrated aircraft capability. The role covers perception, tracking, sensor fusion, mission-level decision making, planning, and integration with flight-control, mission-system, onboard-compute, and payload interfaces. This engineer leads assigned autonomy efforts from initial design through simulation, integration, ground test, and flight test, with responsibility for technical decisions, software quality, performance, and resolving issues across system interfaces. This is a hands-on senior individual contributor position with technical leadership and mentorship responsibilities. The engineer provides guidance to junior and mid-level Machine Learning Engineers through design and code reviews, troubleshooting, and shared development and testing work. The role does not include direct personnel management responsibilities and works closely with Platform Engineers, Flight Test, and aircraft engineering teams.

Requirements

BA/BS degree in Computer Science, Electrical Engineering, Robotics, Aerospace Engineering, or a related technical field, or equivalent practical experience.
5 or more years of relevant engineering experience in autonomy, robotics, machine learning, or related embedded software systems, including hands-on development and deployment of machine learning or autonomy capabilities on physical platforms.
Demonstrated ability to independently lead a significant technical effort from requirements and design through implementation, integration, and testing.
Strong hands-on C++ and Python development skills, including Linux-based development, debugging, profiling, and modern machine learning frameworks.
Substantial experience in one or more of perception, tracking, sensor fusion, autonomous decision making, or planning, with the ability to integrate these capabilities into a complete vehicle system.
Experience defining software architecture and system interfaces, making technical tradeoffs, and resolving integration problems across software and hardware.
Experience deploying and optimizing software or machine learning models on resource-constrained onboard compute.
Experience developing repeatable tests, evaluating performance against defined requirements, and investigating failures using system logs and test data.
Demonstrated ability to provide technical guidance, review designs and code, and mentor other engineers.
Ability to work on-site in Bingen, WA and support hands-on aircraft integration, ground test, and flight test activities.
Professional working proficiency in English, spoken and written.
Familiarity with autopilot and ground control station ecosystems (Applied Navigation Quattro/QET, ArduPilot, PX4, or similar)
Experience serving as test conductor or safety officer for field test operations
Exposure to GNSS performance evaluation, including degraded or contested environments
Exposure to automated or vision-based landing evaluation
Experience with modeling and simulation tools relevant to vehicle performance and dynamics (six-degree-of-freedom simulation, vortex lattice methods, CFD, FEA, or equivalent)
Familiarity with requirements traceability and verification and validation practices
Familiarity with ADS-B, detect-and-avoid, and airspace integration considerations for uncrewed systems testing
Experience with test instrumentation design and integration on space- and weight-constrained platforms
FAA Part 107 certification or equivalent operational credentials
Experience working in multidisciplinary engineering teams in a rapid development environment
This position is not eligible for visa sponsorship. Applicants must be legally authorized to work in the United States without employer sponsorship, now or in the future.
If this role requires access to information controlled under U.S. export control laws (ITAR/EAR), applicants must also qualify as a "U.S. person" under 22 CFR §120.62 (U.S. citizen, U.S. national, lawful permanent resident, or protected individual such as an asylee or refugee).

Nice To Haves

Direct experience delivering UAS autonomy capabilities through aircraft integration and flight test.
Experience with NVIDIA embedded platforms, CUDA, TensorRT, ROS 2, MAVLink, or comparable robotics and vehicle interfaces.
Experience with EO and IR perception, target tracking, geolocation, or navigation under degraded sensor conditions.
Experience with SIL/HIL environments, scenario-based testing, and regression testing of autonomous behaviors.
Experience with model quantization, hardware-accelerated inference, and maintaining model performance across changes in sensors or operating conditions.
Experience integrating autonomy with operator workflows, mission planning, and GCS/C2 systems.
Aerospace or defense experience and eligibility to obtain a DoD Secret clearance.

Responsibilities

Own the technical execution of assigned autonomy capabilities, including requirements development, architecture, implementation, integration, verification, and delivery.
Translate mission needs into defined autonomous behaviors, operating constraints, measurable performance requirements, and test acceptance criteria.
Make and document technical tradeoffs across machine learning, deterministic logic, state machines, behavior trees, planners, and optimization methods based on mission needs, system constraints, and test evidence.
Design and implement mission-level autonomous behaviors, including mission execution, replanning, contingency handling, and coordination with operator commands and aircraft operating limits.
Lead development and integration of perception capabilities, including object detection, classification, tracking, scene understanding, sensor fusion, and geospatial reasoning.
Define and maintain interfaces between autonomy software, autopilots, companion and mission computers, sensors, payloads, data links, and GCS/C2 systems, including clear boundaries between mission autonomy and flight-control authority.
Develop and review production-quality C++ and Python software for real-time and near-real-time execution. Evaluate and optimize latency, throughput, memory use, and compute utilization on NVIDIA GPU and embedded platforms.
Own the model development and deployment process for assigned capabilities, including dataset quality, training and evaluation methods, regression testing, versioning, deployment, and rollback.
Define behavior for degraded sensors, uncertain perception outputs, compute limitations, and interrupted communications, including fallback behavior and operator intervention.
Develop verification strategies using simulation, SIL, HIL, bench testing, and automated testing to evaluate nominal, degraded, and failure conditions before flight.
Lead autonomy software integration and troubleshooting during ground and flight test. Analyze logs and test data, identify root causes, and drive corrective actions through verification.
Break complex efforts into achievable milestones, identify technical risks and dependencies, and communicate progress and tradeoffs to engineering leadership.
Mentor junior and mid-level Machine Learning Engineers through hands-on development, design and code reviews, debugging, and test planning, helping them build technical judgment and take on increasing responsibility for deployed capabilities.
Coordinate technical work across Machine Learning, Platform Engineering, Flight Test, avionics, electrical, mechanical, and other engineering teams.
Other duties as assigned.