Software Engineer, R&D Platforms

Antares•Los Angeles, CA

2d•$112,000 - $200,000

About The Position

Antares is seeking a Research & Development Software Engineer to build the software systems that enable fast, rigorous experimental engineering across our R&D organization. This role supports test campaigns, prototype development, lab systems, engineering analysis, and verification and validation activities by creating the tools, automation frameworks, data workflows, and hardware/software interfaces that allow engineers to collect high-quality data and make better technical decisions. The ideal candidate is a strong software engineer who is comfortable near hardware: someone who can write production-quality Python and C++, debug DAQ or networking issues in a lab, structure experimental data, build tools that other engineers actually use, and bring software engineering discipline to a fast-moving R&D environment. We are looking for someone who can operate across the stack, from quick analysis scripts to durable internal tools and test infrastructure, while helping establish the practices and alignment needed for R&D software and V&V workflows to scale.

Requirements

Bachelor’s degree in Software Engineering, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, Systems Engineering, Physics, EECS, Nuclear Engineering, or a related technical field.
4+ years of experience developing software for engineering, test, R&D, lab, data acquisition, hardware integration, automation, simulation, or analysis environments.
Strong programming ability in Python and C++.

Nice To Haves

Experience building software that interfaces with hardware, instruments, sensors, controllers, databases, APIs, or engineering tools.
Experience with test automation, data acquisition, data processing, or engineering analysis workflows.
Familiarity with software engineering fundamentals: version control, code review, testing, debugging, logging, documentation, and maintainable design.
Ability to work directly with engineers and translate ambiguous R&D needs into useful software tools.
Strong data analysis skills and ability to reason from experimental results.
Strong written and verbal communication skills.
Experience providing technical leadership, mentorship, or lightweight people management while remaining hands-on.
Experience aligning software, test, data, and engineering workflows across R&D, hardware, analysis, controls, manufacturing, and operations teams.
Experience with Python scientific and data tooling such as NumPy, pandas, SciPy, matplotlib, Plotly, Dash, Jupyter, or similar.
Experience interfacing software with DAQ systems, lab instruments, sensors, serial communication, CAN, Modbus, TCP/IP, OPC UA, MQTT, SCPI, VISA, or similar protocols.
Experience with NI hardware, LabVIEW, NI-DAQmx, cDAQ systems, or equivalent instrumentation platforms.
Experience with HIL, SIL, simulation integration, test automation, or digital engineering workflows.
Experience in regulated, safety-critical, aerospace, defense, automotive, nuclear, or other high-consequence engineering environments.
Familiarity with requirements traceability, test evidence, configuration management, and engineering quality systems.
Ability to operate across the stack, from quick scripts to production-quality internal tools, lab interfaces, data infrastructure, and user-facing engineering workflows.

Responsibilities

Build software-hardware interface tools that support R&D testing, experimental campaigns, prototype development, and engineering analysis.
Develop test automation frameworks for lab equipment, instrumentation, DAQ systems, sensors, controllers, power supplies, and prototype hardware.
Create software interfaces to collect, process, store, visualize, and report engineering test data.
Build internal tools for experiment setup, test execution, data review, anomaly detection, and post-processing.
Develop Python-based workflows for data acquisition, analysis, visualization, and automated reporting.
Collaborate with mechanical, electrical, nuclear, controls, manufacturing, and test teams to understand experimental needs and build tools that fit real workflows and develop best practices.
Support verification and validation (V&V) activities by improving test repeatability, data quality, traceability, and reporting.
Create lightweight but robust software systems and infrastructure that help engineers move quickly without losing configuration control or data integrity.
Debug software, instrumentation, networking, DAQ, and hardware-in-the-loop issues in lab and test environments.
Maintain clear documentation for software tools, interfaces, data schemas, test automation frameworks, and operating procedures.