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About This Role
About Point One Navigation
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Point One Navigation is on a mission to bridge the digital and physical worlds through precision location, with an API\-first, developer\-focused approach. Our RTK corrections network and FusionEngine software deliver centimeter\-level accuracy and high\-confidence positioning for vehicles, robots, drones, and devices across industries in outdoor applications. We are actively broadening our expertise into indoor environments to provide the same high\-standard localization and navigational quality for users everywhere.
Role Outcome
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Staff Computer Vision Engineers are responsible for the comprehensive lifecycle of Point One’s spatial AI and visual navigation features, overseeing everything from initial camera integration and image processing to high\-level architectural and algorithmic design.
This is an ownership\-first role: you will conceptualize and drive complex technical challenges end\-to\-end \- from early architecture through deployment in mission\-critical systems \- while raising the technical bar across the team.
FusionEngine already powers a wide range of devices, hardware platforms, and customer applications. The R\&D team is responsible for making sure our vision and perception systems work reliably across all of them: Designing solutions robust to visually challenging environments, optimizing models for compute\-constrained edge devices, and ensuring our algorithms stay thoroughly tested, verified, and production\-ready as we scale.
Success in this role means:
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- State\-of\-the\-art CV and SLAM techniques are successfully translated from research papers, internal prototypes, or third\-party solutions into highly performant, production\-grade algorithms.
- Rigorous benchmarking pipelines are established to objectively evaluate internal algorithms against commercial OTS solutions and vendor offerings.
- Vision pipelines automatically generate and maintain accurate, semantically rich maps of complex indoor environments with minimal manual intervention.
- Real\-time localization and multi\-agent tracking (assets, robots, people) are highly robust, minimizing latency and identity switches even in dynamic or visually degraded conditions.
- Spatial data, coordinate frames, and map layers are exposed via clean data models and APIs, empowering our UI and infrastructure teams to build seamless user\-facing applications.
- Junior engineers grow faster and the team's practices improve measurably over time.
Immediate Areas of Focus
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*Applied Research, Benchmarking \& Selection*
- Lead the research, evaluation, and selection of state\-of\-the\-art computer vision, deep learning, and spatial navigation methodologies for highly accurate 3D maps of large\-scale facilities, considering both internal development and third\-party commercial solutions.
- Develop or integrate deep learning and classical CV algorithms to extract semantic information from environments (e.g., structural elements, zones, and specific objects) for overlay onto base map.
- Ensure maps can be dynamically updated over time as the physical layout of a facility changes, enabling map version management and consistency.
- Design and own a rigorous benchmarking framework to continuously evaluate the accuracy, latency, compute footprint, and reliability of internal code versus off\-the\-shelf and vendor technologies.
- Rapidly prototype new perception capabilities and architect their transition into highly optimized, edge\-capable production code, or seamlessly encapsulate and integrate verified third\-party modules.
- Collaborate tightly with infrastructure and UI engineers to manage data products, render maps, and track assets for the end user.
*Drive Real\-Time Localization and Tracking*
- Understand how and work with the larger navigation team to use camera data with GNSS, IMU, wheel odometry, and other indoor positioning signals to maintain high\-confidence state estimation for moving agents in all environments.
- Drive performance tuning for edge deployment to ensure tracking algorithms run with low latency and high reliability on constrained compute architectures.
- Proactively identify failure modes in tracking and mapping and design robust algorithmic fallbacks.
*Raise the Technical Bar*
- Mentor junior engineers and establish best practices across the team.
- Contribute to architecture discussions, technical strategy, and roadmap planning.
Qualifications
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- 7\+ years of professional algorithm and software development experience, with significant depth in applied research, computer vision, or robotics.
- Expertise in modern C\+\+ (C\+\+14 or later) and Python, with a demonstrated history of success of taking AI model prototypes (PyTorch, TensorFlow) and turning them into scalable, real\-time production systems.
- Expertise in ROS1/ROS2\.
- Hands\-on experience with Visual SLAM, 3D reconstruction, and mapping architectures.
- Experience in deploying semantic segmentation/object detection in real\-world environments.
- Experience with multi\-view geometry, camera calibration, and fusing vision with other sensor modalities (IMU, GNSS).
- Ability to take high\-level research and business goals and decompose them into actionable engineering tasks, realistic schedules, and clear milestones.
- MS or PhD in Computer Science, Robotics, or equivalent experience.
*Bonus Points For*
- Background in deploying optimized vision models to edge devices using TensorRT, ONNX, or platform\-specific accelerators.
- Experience in deploying multi\-object tracking and ReID architectures in real\-world, dynamic environments.
- Familiarity with managing large\-scale point clouds, mesh generation, or NeRFs/Gaussian Splatting for environmental representation.
Our Cultural Foundation
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At Point One, our cultural and operating design is built around one guiding principle: we must move with extreme speed and efficiency of effort to stay in a leadership position.
This environment gives people a high level of autonomy and the ability to make a real impact. It also challenges every team member to grow — both professionally and personally. Because we focus on promoting from within rather than relying on external hiring, the opportunities for advancement are tremendous for those who seek them.
That said, growth only comes from delivering in the present. What matters most is the job to be done today, not the job you want tomorrow. When we all focus on today’s outcomes with excellence, the path to greater responsibility and growth naturally follows.
We think about our culture in two dimensions:
How We Show Up Every Day
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These are the behaviors we expect every team member to bring to work — the foundation of being a consummate, high\-output teammate:
- Trust / Assume Best Intent — Trust allows us to move fast. When we start from trust, we spend no time second\-guessing or looking for ulterior motives and thus focus all our energy on acting.
- High Output, Action Oriented — Our default posture is “yes.” We bias toward action and deliver results quickly, knowing that speed and efficiency compound into impact as we unblock others around us.
- Divine Discontent — We’re never satisfied with the status quo and are self\-motivated to improve ourselves, our work, and our company. We actively seek feedback in real\-time to shorten improvement cycles.
- No Ego, One Team — Collaboration without ego creates leverage. When we win as one team, we eliminate friction and move faster together.
- Self Accountability — Taking ownership is the straightest line to learning, self\-improvement, and correcting our course of action. And blaming others around us is a fast path to destroying trust.
Operating Principles
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These are the systems and norms that amplify speed and efficiency at the company level:
- Edge Innovation — We bias toward action over approval. Experiment, decide, and move — failure is just a step toward faster learning.
- No Hierarchies — We practice self\-prioritization and go direct to the source. Flattening layers reduces drag and maximizes autonomy.
- Customer Experience First — We optimize for the end\-to\-end customer outcome, not functional or departmental efficiency. This focus cuts waste, aligns priorities, and ensures we spend effort where it matters most.
If this role sounds like a fit, we’d love to hear from you. Apply below and join us in shaping the future of precise location.
Compensation Range: $215,270 \- $265,800
Salary Context
This $215K-$265K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Point One Navigation, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($240K) sits 10% above the category median. Disclosed range: $215K to $265K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Point One Navigation AI Hiring
Point One Navigation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $265K - $265K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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