Interested in this AI/ML Engineer role at Blue River Technology?
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About This Role
We're Blue River, a team of innovators driven to create intelligent machinery that solves monumental problems for our customers. We empower our customers – farmers, construction crews, and foresters \- to implement safer and more sustainable solutions, driving increased profitability with less reliance on scarce labor. We believe that focusing on the small stuff – pixel\-by\-pixel and task\-by\-task \- leads to big gains. With our partners at John Deere, we have the ability to bring innovative computer vision, machine learning, robotics, and product management solutions to scale production, maximizing their potential impact.
Our people are at the heart of what we do. Through cross\-disciplinary collaboration, this mission\-driven and daring team is eager to define the new frontier of mobile robotics. We are always asking hard questions, rapidly iterating, and getting our boots in the field and on\-site to figure it out. We won't give up until we've made a tangible and positive impact on the planet.
Summary
Our mission is to bring the power of end\-to\-end machine learning and robotics to John Deere, revolutionizing how robotic systems are built and opening up previously inaccessible opportunities. This involves multiple fronts. We are building products with short\-term value to deploy hardware and software on customer vehicles and build the necessary datasets. Additionally, we are developing end\-to\-end robotics applications and the accompanying playbook.
As a Senior ML Infrastructure Engineer, you will help grow and shape our E2E stack. This involves shaping the design of the E2E training and inference pipeline, both on and off vehicle, on and off prem. You will also help with new models, features, and augmentations. This is a lean team moving quickly. You should be comfortable and excited about working with ambiguity, helping define what will move the program forward, working across traditional boundaries, and learning new things.
- Employment Type: Full\-Time
- Work Location: Santa Clara, CA preferred. If remote, you will need to occasionally travel (10\-15%) to work with the team and visit the field.
- Visa sponsorship is available for this position.
Job Responsibilities
The main job responsibilities include:
- Design and own our data pipeline, from ingestion to training to search to reinforcement\-learning correction data, and hopefully one day to simulation.
- Grow the scale of our training pipeline from 10's of hours of data to 1000's of hours of data.
- Design extensible software to work across many vehicle platforms and jobs.
- Evaluate new models and model architectures.
- Work closely with ML engineers and robotics engineers, collaborate to solve cross\-disciplinary problems, and improve performance.
- Make our project and customers successful.
- Be an ambassador for our needs and help steer us towards an E2E\-first organization.
Required Experience and Skills
- 4\+ years of professional experience developing machine learning infrastructure, data platforms, robotics software, or related systems.
- Proven track record of delivering complex data pipelines in production environments.
- Strong background with ML pipelines and system architectures.
- Strong Python coding skills.
- Comfortable working across traditional team boundaries to deliver results.
Preferred Experience and Skills
- Experience with robotics middleware such as ROS or other robotics\-focused software packages.
- Experience with large\-scale agricultural, construction, or heavy machinery systems.
- Strong CUDA background or other GPU frameworks.
At Blue River, we're passionate about creating an inclusive workplace that promotes and values diversity. While we have more work to do to advance diversity and inclusion, we're investing in our programs, including recruiting, mentorship, career development, and learning \& development to ensure they support our Diversity, Equity, and Inclusion goals. We support each employee in living a full life, enabling a thriving career, and accomplishing a meaningful, challenging mission while collaborating with incredible people. We are dedicated to building a diverse and inclusive workplace, so if you're excited about this role but your experience doesn't align completely with the job description, we encourage you to apply anyway.
We are an equal\-opportunity employer and do not discriminate based on race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment. Please contact us to request an accommodation.
The US annual base salary range for this position is $160,000 \- $287,000, along with eligibility for Blue River's bonus and benefit programs.
Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job\-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process. During the recruitment process, we may identify an alternative role or level to which you are more suited. If your ideal role at Blue River differs from the advertised position, we will provide an updated pay range as soon as possible during the hiring process.
Salary Context
This $160K-$287K 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 Blue River Technology, 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 ($223K) sits 21% above the category median. Disclosed range: $160K to $287K.
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.
Blue River Technology AI Hiring
Blue River Technology has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, US. Compensation range: $287K - $287K.
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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