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About This Role
About Mach Industries
=========================
Founded in 2022, Mach Industries is a rapidly growing defense technology company focused on developing next\-generation autonomous defense platforms. At the core of our mission is the commitment to delivering scalable, decentralized defense systems that enhance the strategic capabilities of the United States and its allies. With a workforce of approximately 350 employees, we operate with startup agility and ambition.
Our vision is to redefine the future of warfare through cutting\-edge manufacturing, innovation at speed, and unwavering focus on national security. We are dedicated to solving the next generation of warfare with lethal systems that deter kinetic conflict and protect global security.
The Role
============
Mach Industries is building an AI\-forward autonomy stack for contested environments where GPS and other sensing are unavailable or unreliable. As a Machine Learning Engineer, you will own and scale the training, data, and edge\-inference backbone that every vision and multi\-sensor model on our product lines depends on for detection, tracking, search, navigation, targeting, and automatic target recognition. This is a broad, high\-ownership role: you'll stand up the data and training infrastructure that lets the autonomy team iterate fast, generate synthetic data to cover the long tail, and get research\-grade models running in real time on embedded hardware in flight. We are generalists, so you'll move fluidly between infrastructure, modeling, and deployment.
Key Responsibilities
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- Own and evolve the training and data infrastructure the autonomy team builds on: ingestion from flight/sim/HITL, curation and mining, labeling/QA workflows, dataset versioning (DVC/Parquet), and reproducible dataset builds.
- Stand up and scale training/eval infrastructure: distributed multi\-GPU training, experiment tracking, a model registry, and CI\-based evaluation with regression gates plus automated field\-data to retrain to validate to redeploy loops.
- Deploy and optimize models for real\-time edge inference on Jetson\-class hardware (quantization/pruning, TensorRT/ONNX Runtime); profile CPU/GPU and hit tight latency, throughput, and SWaP targets.
- Build and improve models across the portfolio as a hands\-on IC: detection, segmentation, tracking, target/area search, classification/ATR, and multi\-sensor fusion for EO/IR and auxiliary sensing.
- Generate and manage synthetic data at scale (simulation \+ domain randomization) to cover long\-tail and degraded conditions and close sim\-to\-real gaps.
- Instrument runtime health, drift detection, and graceful degradation, and wire model\-performance metrics back into the data and retraining loop.
- Live close to flight data with visualization, triage, and root\-cause tooling so the team can go from field logs to insight and model updates rapidly.
- Partner with other autonomy disciplines across perception, localization, embedded, and flight\-test to take capabilities from prototype to sim to HITL to flight to deployment.
Required Qualifications
===========================
- Strong generalist software engineering: Python for ML and tooling, plus production C\+\+ on Linux; profiling, optimization, and rigorous testing discipline.
- Proven experience building ML data and training pipelines end to end: dataset construction, labeling/QA, augmentation, experiment tracking, and reproducible training.
- Hands\-on training and fine\-tuning in PyTorch across modern 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 embedded GPU (Jetson\-class) hardware.
- Data and MLOps infrastructure: SQL/Parquet, dataset/versioning tools, CI\-based validation, and scalable multi\-GPU training.
- BS/MS/PhD in CS/EE/Robotics or similar, or equivalent experience, with a track record shipping ML models to production or hardware. Senior candidates: deeper ownership of training/data infrastructure at scale.
Preferred Qualifications
============================
- Synthetic data generation and simulation (e.g. Unreal/Isaac, domain randomization) and demonstrated sim\-to\-real transfer.
- EO/IR imagery experience and working with real flight/test data in challenging, degraded, or contested environments.
- Multi\-modal perception and fusion (EO/IR \+ radar/LiDAR/RF) at the feature or decision level.
- Detection/tracking/search at scale; active learning and data\-mining strategies for long\-tail coverage.
- CUDA backends for performance debugging; ROS 2; NVIDIA Jetson deployment pipelines.
- Drift/dataset\-shift monitoring, robustness and rare\-event testing, long\-horizon reliability metrics.
- Distributed training frameworks and cloud ML platforms (e.g. SageMaker); Docker for reproducibility; Rust for systems tooling.
Disclosures
This position may require access to information protected under U.S. export control laws and regulations, including the Export Administration Regulations (EAR) and the International Traffic in Arms Regulations (ITAR). Please note that any offer for employment may be conditioned on authorization to receive software or technology controlled under these U.S. export control laws and regulations without sponsorship for an export license.
Mach participates in E\-Verify and will provide the federal government with your Form I\-9 information to confirm that you are authorized to work in the U.S.
The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offers may vary based on (but not limited to) work experience, education and training, critical skills, and business considerations. Highly competitive equity grants are included in most offers and are considered part of Mach’s total compensation package. Mach offers benefits such as health insurance, retirement plans, and opportunities for professional development.
Mach is an equal opportunity employer committed to creating a diverse and inclusive workplace. All qualified applicants will be treated with respect and receive equal consideration for employment without regard to race, color, creed, religion, sex, gender identity, sexual orientation, national origin, disability, uniform service, Veteran status, age, or any other protected characteristic per federal, state, or local law, including those with a criminal history, in a manner consistent with the requirements of applicable state and local laws. If you’d like to defend the American way of life, please reach out!
Compensation Range: $120K \- $160K
Salary Context
This $120K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At MACH INDUSTRIES, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($140K) sits 35% below the category median. Disclosed range: $120K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
MACH INDUSTRIES AI Hiring
MACH INDUSTRIES has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Huntington Beach, CA, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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