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About This Role
### THE COMPANY
Saildrone is a maritime defense company and the global leader in autonomous unmanned surface vehicles (USVs). With more ocean miles and real\-world operational experience than any USV manufacturer or operator, Saildrone maintains active, combat\-deployed systems supporting national security and force projection around the world, 24/7/365\.
Saildrone's manufacturing and R\&D headquarters are located in Alameda, CA, with business development and sales operations in Washington, DC, and deployment hubs in Europe and the Middle East. By combining proven autonomous operations, edge computing, advanced sensing, renewable power, and the most advanced and robust unmanned surface technology on the planet, Saildrone is shaping how the Navy of the future operates. Join a fast\-moving, mission\-driven team at the forefront of maritime security and autonomous innovation.
THE POSITION
Saildrone is seeking a Staff Machine Learning Engineer to join our team. Reporting directly to the Director of Software Engineering, you will play a critical role in designing, deploying, and scaling machine learning systems that enable autonomy and real\-time intelligence across Saildrone's global fleet. You will expand Saildrone's model portfolio and ensure reliable, high\-performance inference on edge hardware in complex maritime environments. We are looking for a technical leader who creates clarity from ambiguity, drives end\-to\-end execution, and takes ownership of production ML systems in mission\-critical environments.
THE TEAM
The Machine Learning team is responsible for developing and deploying models that power perception, autonomy, and intelligence across Saildrone's autonomous surface vehicles. We focus on building scalable, high\-performance ML systems that transform multimodal sensor data into actionable insights, enabling persistent maritime awareness in national security and defense environments.
THE RESPONSIBILITIES
- Production \& Mission Impact: Design and deploy production\-grade ML models for real\-time perception to detect, classify, and track high\-value targets. Your work directly enables USVs to operate 24/7/365 in harsh, remote, and hostile maritime environments.
- Edge Architecture \& Autonomy: Own the full ML lifecycle architecture, ensuring models run reliably on NVIDIA Jetson/AGX platforms. Drive the intelligence that advances autonomous decision\-making and behavior for a growing fleet of robotic systems.
- Multi\-Modal Sensor Fusion: Lead the integration of Saildrone's unique sensor suite—including cameras, radar, lidar, hydrophones, and bathymetric sensors—to maintain situational awareness in complex, resource\-constrained environments.
- Dataset \& Robustness Engineering: Drive the rapid expansion of proprietary maritime datasets and develop rigorous evaluation frameworks to ensure model performance remains stable across variable sea states and extreme weather.
- Strategic ML Ops: Architect and scale cloud\-based training pipelines and CI/CD workflows. You will resolve technical ambiguity to ensure the "full stack" from data ingestion to edge deployment is performant and maintainable.
- System\-Wide Optimization: Lead technical decisions for onboard compute efficiency using runtime libraries like TensorRT, ensuring that rapid model iterations enhance rather than disrupt the overall software stack stability.
- Technical Leadership \& Vision: Direct large\-scale ML projects from concept to completion. Mentor junior and senior engineers while shaping the technical roadmap for the global ML organization. Set engineering standards and influence architectural direction across multiple ML and autonomy teams
- Cross\-Functional Integration: Serve as a technical leader across multiple engineering organizations —from Perception to Frontend—to ensure ML\-driven insights are actionable for mission pilots and critical to disrupting illegal maritime activity.
THE QUALIFICATIONS
- BS or MS in Computer Science, Electrical Engineering, or a related technical field, as required for the design of complex autonomous systems.
- 10\+ years of experience in Machine Learning or Software Engineering, performing work related to the full ML lifecycle, from cloud\-based training to edge deployment.
- Serve as a technical authority for edge AI systems operating in mission\-critical environments.
- Track record of leading cross\-functional technical initiatives in autonomy, robotics, defense, or large\-scale distributed systems
- Demonstrated proficiency in Python, ML frameworks (PyTorch/TensorFlow), and runtime libraries (TensorRT) required to deploy performant computer vision and sensor fusion models.
- Experience making system\-level architecture and modeling decisions under high ambiguity, balancing performance, reliability, and compute constraints in real\-world deployed ML systems
- Track record of communicating technical vision, project roadmaps, and model performance metrics to peers, senior leadership, and cross\-functional partners.
- Ability to work effectively in remote or hybrid environments, including supporting a fleet that operates 24/7/365 in harsh and hostile maritime conditions.
- Working knowledge of CI/CD best practices, Linux/Unix environments, and MLOps pipelines relevant to maintaining production\-grade software at scale.
- Demonstrated experience mentoring senior and junior engineers and leading end\-to\-end technical projects from initial concept to mission\-ready fielding.
To view Saildrone's candidate privacy policy, please visit: https://www.saildrone.com/privacy.
### BENEFITS
At Saildrone, we're building operational capability that matters—and that requires people who can do their best work over the long term. We invest in our team's well\-being, financial security, and professional growth so you can focus on delivering real\-world impact with confidence and stability. Our comprehensive benefits package is designed to support you and your family while you contribute to a mission with lasting significance.
- Generous Time Off: Competitive Paid Time Off (PTO) accrual plus a robust annual holiday schedule and paid sick leave ensure you can rest, reset, and sustain performance over time.
- Comprehensive Health Coverage: Premium, multi\-tier Medical, Dental, and Vision plans with significant company contributions for employees and dependents—providing security and peace of mind.
- Shared Ownership in the Mission: Equity grants are a core part of our compensation, allowing you to participate in the long\-term value you help create through meaningful, operational work.
- Retirement Savings: Access to a 401(k) retirement plan with flexible pre\-tax and Roth payroll contribution options supports long\-term financial planning.
- Investment in Your Growth: We support your continuous learning through an annual professional development reimbursement program, empowering you to sharpen your skills and stay ahead of the curve in a rapidly evolving field.
- Relocation Support: For eligible roles, we offer relocation assistance to help bring the world's best talent to our mission\-critical locations.
Saildrone is an equal opportunity employer that is committed to diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.
Salary Context
This $215K-$270K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Saildrone, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($242K) sits 31% above the category median. Disclosed range: $215K to $270K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Saildrone AI Hiring
Saildrone has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alameda, CA, US. Compensation range: $270K - $270K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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