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About This Role
Role: Senior Machine Learning Engineer
Location: San Diego, CA (in\-office)
Salary Range: $180,000 \- $250,000 / yr \+ stock options, 401k matching, and other benefits
Role Overview:
Seasats' vehicles operate in highly remote and often communications\-limited environments. To complete persistent missions at scale, the vehicles must be able to perceive and reason about the world around them without humans in the loop. Seasats has built a reliable and increasingly intelligent autonomy stack incorporating onboard sensors and processing, and machine learning is becoming central to how it evolves.
In this role, you'll own the development, training, and edge deployment of the perception models at the heart of that stack. You'll take models from experiment to production: curating fleet data, training and distilling models, optimizing them for embedded hardware, and validating them against real\-world maritime conditions. Your work will directly determine how safely and intelligently our vessels navigate dynamic ocean environments.
Role Details:
As a senior member of the team, you'll help set the technical direction for onboard ML at Seasats and raise the bar for how models are built, evaluated, and shipped across the company. You'll work independently on experiments while collaborating closely with the vehicle software team to integrate your work into the larger stack.
On a day\-by\-day basis, you will:
- Help define and drive the ML roadmap for vehicle perception
- Scope and run ML experiments with clearly measurable end states that would positively impact vehicle performance if successful
- Train, fine\-tune, and optimize models sized for compute\-limited edge hardware, and validate real\-time performance on target hardware
- Build and maintain the data pipeline: analyzing, organizing, and labeling datasets from our fleet to drive data\-driven development
- Establish evaluation frameworks and metrics that give the team confidence in model behavior before it goes to sea
This is an excellent opportunity to do high impact work, see your models running live on vehicles at sea, and join a fun and hard\-working team on the cutting edge of ocean autonomy.
About You:
- 7\+ years in machine learning, including 5\+ years solving perception problems in production systems
- Deep computer vision background, including foundation models and training smaller custom models
- Track record of shipping performant ML models to edge devices and hands\-on experience with model optimization for the edge (quantization, pruning, and inference runtimes such as TensorRT or ONNX Runtime)
- Strong proficiency in Python, with expert\-level fluency in PyTorch and strong working knowledge of OpenCV
- Experience with MLOps tooling for dataset versioning, experiment tracking, and model deployment
- Experience working with real\-time sensor data (e.g., camera, radar, and IMU streams) and familiarity with classical perception techniques (e.g., filtering, tracking, sensor fusion
- Demonstrated ability to scope projects independently, lead technical efforts, and mentor other engineers
- Strong technical communication skills
- A bias toward getting multiple projects to 80% rather than one project to "perfect", while taking great joy in seeing projects approach perfection over time
In addition, it's nice (though not essential) if you have experience working with:
- Probabilistic algorithms for obstacle and target tracking, and change detection algorithms for a variety of data types
- Real\-time multi\-modal sensor fusion across LiDAR, radar, camera, and IMU data
- Maritime and/or acoustic data
- Path\-planning and control algorithms
About Seasats:
At Seasats, we're passionate about delivering maritime robotics solutions to redefine the maritime industry. Our primary products are unmanned surface vehicles (USVs), designed to carry sensors at sea for months at a time. Our USVs provide persistent monitoring and data acquisition to defense, scientific, and commercial customers, and have autonomously crossed both the Pacific and Atlantic oceans. After thousands of years in which the only way to gather information from the ocean was to put people on a boat, these uncrewed vessels are transforming how humanity monitors and interacts with the ocean. Here, you'll find the space and opportunity to do your life's best work.
Along with your salary, you'll receive perks including:
- Stock options
- Competitive insurance (including a 99% employer\-covered Gold HMO plan or other options)
- 401k matching up to 4% of salary
- Four free lunches per week
- An employee activity fund
- A pet\-friendly office
- Unlimited/Flex PTO
Hiring Notes:
When applying, you'll be asked to provide a resume and answer a few screening questions.
Due to export control requirements applicable to this position, we are only able to consider U.S. persons, as defined by U.S. export control laws. This includes U.S. citizens, lawful permanent residents, and individuals granted asylee or refugee status.
We appreciate diverse perspectives and life experiences, and we're committed to building a team that reflects a wide range of backgrounds. Seasats provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination of any type based on race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, protected veteran status, or any other characteristic protected under federal, state, or local law.
We look forward to reviewing your application!
Salary Context
This $180K-$250K range is above 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 Seasats, 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $180K to $250K.
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.
Seasats AI Hiring
Seasats has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $250K - $250K.
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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