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About This Role
Company Overview:
Joby Flight Research designs, develops, and flight\-tests novel aircraft using a software\-first autonomy approach. We build and deploy autonomy, perception, planning, and radar systems across conventional, electric, and hydrogen\-electric aircraft in both CTOL and VTOL configurations.
Overview:
Joby Flight Research is seeking a Senior Machine Learning Engineer to build state\-of\-the\-art perception and reasoning algorithms into the Superpilot™ autonomy stack, enabling autonomous aircraft and associated ground systems to safely and autonomously navigate their complex environment, and define the standard of autonomous flight.
In this role, you will be responsible for training models that enhance current Superpilot™ algorithms while investigating the application of cutting\-edge research to expand autonomous flight capabilities. Your responsibilities will include filtering sensor data from flights, architecting dependable training infrastructure for datasets and models, and performing ongoing evaluations within our Operational Design Domain (ODD). By focusing on these tasks, you will play a vital part in achieving the detection and localization standards necessary for ensuring the safety of autonomous aviation.
We are a small, high\-impact team that values curiosity, technical initiative, and the ability to operate independently. You will collaborate deeply with controls, and flight software engineers to build a foundation that accelerates our path to safe, autonomous flight. The right candidate is a strong ML engineer who cares deeply about system integration, experiment reproducibility and traceability, and is able to contribute to different parts of the data pipeline where needed.
Responsibilities:
- Architect and deploy sophisticated algorithms for aircraft environment detection and tracking. By integrating deep learning with geometric computer vision, you will utilize multi\-sensor inputs—including lidar, radar, and varied camera systems—to establish comprehensive 3D situational awareness.
- Construct high\-performance model training pipelines and extensive evaluation systems. These frameworks must ensure reliability by identifying performance nuances in complex edge cases and rare operational scenarios.
- Work across multi\-disciplinary teams—including controls, systems, and flight testing—to seamlessly embed and validate algorithms within our concept of operations.
- Drive continuous improvement of perception stacks through simulation. You will focus on optimizing latency and robustness to ensure peak performance during demanding flight conditions.
- Engineer specialized diagnostic and visualization tools to extract meaningful insights from field data. This involves facilitating swift root\-cause analysis and resolving perception challenges in active deployments.
- Shape the future of the Superpilot™ autonomy system. Your contributions will define a perception architecture that sets new benchmarks for safety and reliability in the autonomous aviation industry.
Required:
- Proven Professional Background: At least 6 years of experience developing and implementing advanced perception frameworks for autonomous platforms, including aircraft, vehicles, or robotics.
- Bachelor's degree \+8 years experience; Master's degree \+ 6 years experience; PhD degree \+ 3 years experience.
- Deep Learning Specialization: Extensive practical knowledge of cutting\-edge models for tracking and detecting objects within real\-world applications.
- Computer Vision Proficiency: Comprehensive understanding of geometric vision methods such as visual odometry, structure\-from\-motion, and stereo vision to facilitate accurate 3D estimation.
- Technical Toolset: Competency in C\+\+, Python, and PyTorch, along with various deep learning inference and training ecosystems.
- Problem\-Solving Aptitude: A creative approach to engineering that includes a history of mitigating performance delays and addressing complex operational edge cases.
- Operational Support: Direct experience maintaining commercial autonomous systems by diagnosing technical hurdles and improving live system dependability.
- Collaborative Communication: Strong interpersonal skills necessary to succeed within a fast\-paced, multidisciplinary team focused on safety\-critical engineering.
Desired:
- Expertise in developing supporting data curation and model experimentation pipelines to support ML experimentation.
- Experience deploying models onto GPUs and other accelerators for production\-level embedded systems.
- Experience with autonomous vehicles
- Experience in processing aircraft data (GPS, inertial, air data, radio data, etc.)
- Expert\-level software engineering: deep expertise in architecting and writing clean, scalable, and maintainable code
- Experience with version control and CI/CD platforms, able to manage your software through its entire lifecycle (development, testing, deployment)
- Experience deploying ML models in a production environment using modern MLOps principles and tools (e.g., MLflow, Kubeflow)
Compensation at Joby is a combination of base pay and Restricted Stock Units (RSUs). The target base pay for this position is $202,100 \- $245,000/yr. The compensation package will be determined by job\-related knowledge, skills, and experience. Joby also offers a comprehensive benefits package, including paid time off, healthcare benefits, a 401(k) plan with a company match, an employee stock purchase plan (ESPP), short\-term and long\-term disability coverage, life insurance, and more.
Additional Information:
Joby Aviation is an equal opportunity employer.
Salary Context
This $202K-$245K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Joby Aviation, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($223K) sits 23% above the category median. Disclosed range: $202K to $245K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Joby Aviation AI Hiring
Joby Aviation has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Santa Cruz, CA, US. Compensation range: $245K - $245K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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