Mach

1 day ago

Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 58 minutes agoStatus: Live

Job Summary

Own and scale the training, data, and edge-inference backbone for autonomy models used in detection, tracking, search, navigation, targeting, and automatic target recognition. Build end-to-end ML data pipelines (ingestion to labeling, QA, dataset versioning, reproducible builds), stand up distributed multi-GPU training/eval with CI regression gates, and deploy optimized models to Jetson-class hardware using TensorRT/ONNX Runtime.

Location: United States

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Own and evolve training/data infrastructure, including flight/sim/HITL ingestion, curation/mining, labeling/QA, dataset versioning (DVC/Parquet), and reproducible dataset builds.
•Stand up and scale training/evaluation infrastructure: distributed multi-GPU training, experiment tracking, model registry, and CI-based evaluation with regression gates plus field-data retraining loops.
•Deploy and optimize models for real-time edge inference on Jetson-class hardware using quantization/pruning and TensorRT/ONNX Runtime, profiling CPU/GPU and meeting latency/throughput/SWaP targets.
•Develop and improve portfolio models as a hands-on IC across detection, segmentation, tracking, search, classification/ATR, and multi-sensor fusion for EO/IR and auxiliary sensing.
•Generate and manage synthetic data at scale (simulation + domain randomization), instrument runtime health/drift detection/robustness, and partner with autonomy disciplines from prototype to HITL to flight deployment.

Pay and Benefits

Salary: USD 120,000 - 160,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceRetirement

Key Requirements

•Strong generalist software engineering: Python for ML/tooling and production C++ on Linux, with profiling, optimization, and rigorous testing.
•Build ML data and training pipelines end to end (dataset construction, labeling/QA, augmentation, experiment tracking, reproducible training).
•Hands-on training and fine-tuning in PyTorch across detection/segmentation/tracking architectures (CNN/Transformer).
•Edge and real-time deployment: model compression (INT8/FP16), runtime optimization (TensorRT/ONNX Runtime), and meeting latency/SWaP constraints on Jetson-class embedded GPUs.
•BS/MS/PhD in CS/EE/Robotics (or equivalent) with a track record shipping ML models to production or hardware; data/MLOps infrastructure (SQL/Parquet, versioning, CI validation, scalable multi-GPU training).

Education:

Tech Stack:PythonC++LinuxPyTorchCUDATensorRTONNX RuntimeONNXJetsonDVCParquetSQLDockerROS 2SageMakerUnrealIsaacRustMulti-GPUExperiment Tracking

Company Brief

Mach

Designs and manufactures unmanned systems, tactical engines, and decentralized defense manufacturing infrastructure (Forge) to scale production of autonomous aerial systems and related propulsion for U.S. defense customers and partners.

Industry: Defense Manufacturing

Company Size: Medium (51 to 250 employees)

Growth: Growth Stage Startup

Valuation: USD 250M to 500M

Funding: Series B

Headquarters: Huntington Beach, United States

Founded: 2023

Glassdoor: 3.9

Website LinkedIn Glassdoor

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Mach

1 day ago

Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 58 minutes agoStatus: Live

Job Summary

Location: United States

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Own and evolve training/data infrastructure, including flight/sim/HITL ingestion, curation/mining, labeling/QA, dataset versioning (DVC/Parquet), and reproducible dataset builds.
•Stand up and scale training/evaluation infrastructure: distributed multi-GPU training, experiment tracking, model registry, and CI-based evaluation with regression gates plus field-data retraining loops.
•Deploy and optimize models for real-time edge inference on Jetson-class hardware using quantization/pruning and TensorRT/ONNX Runtime, profiling CPU/GPU and meeting latency/throughput/SWaP targets.
•Develop and improve portfolio models as a hands-on IC across detection, segmentation, tracking, search, classification/ATR, and multi-sensor fusion for EO/IR and auxiliary sensing.
•Generate and manage synthetic data at scale (simulation + domain randomization), instrument runtime health/drift detection/robustness, and partner with autonomy disciplines from prototype to HITL to flight deployment.

Pay and Benefits

Salary: USD 120,000 - 160,000 annually

Equity and Bonus:Equity

Perks:Health InsuranceRetirement

Key Requirements

•Strong generalist software engineering: Python for ML/tooling and production C++ on Linux, with profiling, optimization, and rigorous testing.
•Build ML data and training pipelines end to end (dataset construction, labeling/QA, augmentation, experiment tracking, reproducible training).
•Hands-on training and fine-tuning in PyTorch across detection/segmentation/tracking architectures (CNN/Transformer).
•Edge and real-time deployment: model compression (INT8/FP16), runtime optimization (TensorRT/ONNX Runtime), and meeting latency/SWaP constraints on Jetson-class embedded GPUs.
•BS/MS/PhD in CS/EE/Robotics (or equivalent) with a track record shipping ML models to production or hardware; data/MLOps infrastructure (SQL/Parquet, versioning, CI validation, scalable multi-GPU training).

Education:

Tech Stack:PythonC++LinuxPyTorchCUDATensorRTONNX RuntimeONNXJetsonDVCParquetSQLDockerROS 2SageMakerUnrealIsaacRustMulti-GPUExperiment Tracking

Company Brief

Mach

Industry: Defense Manufacturing

Company Size: Medium (51 to 250 employees)

Growth: Growth Stage Startup

Valuation: USD 250M to 500M

Funding: Series B

Headquarters: Huntington Beach, United States

Founded: 2023

Glassdoor: 3.9

Website LinkedIn Glassdoor