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About This Role
Full\-Time \| Santa Monica, CA \| On\-Site
About Our Client
Our client is a technology company developing next\-generation intelligent systems at the intersection of AI, XR, robotics, autonomy, and spatial computing. Their products support mission\-critical applications across defense, public safety, and critical infrastructure. They are seeking passionate professionals who thrive in fast\-paced environments and enjoy building impactful products from concept to deployment.
The Role
Our client is seeking a Machine Learning Engineer to help design and implement intelligent systems that extract meaning and predictive value from computer vision and behavioral datasets. This is a junior\-level, in\-person role suited for candidates with 2–3 years of experience and a solid foundation in deep learning, embeddings, and modern neural architectures.
As a member of the AI team, the ideal candidate will work on projects that leverage CNNs, transformer models, and embedding architectures to encode and reason over pose, facial, and action\-based visual data. These systems support downstream tasks such as future action prediction, semantic matching, and similarity\-based inference.
Key Responsibilities
- Design and implement machine learning pipelines that encode visual input (pose, face, object/classification) into shared embedding spaces for similarity and predictive tasks.
- Build and fine\-tune convolutional and transformer\-based neural architectures optimized for visual recognition and representation learning.
- Develop encoding and embedding techniques that allow consistent comparison across multiple data types (e.g., pose vectors, facial landmarks, class labels).
- Apply techniques such as cosine similarity, distance metrics, and latent clustering to perform behavioral inference and action prediction.
- Contribute to model training, evaluation, and deployment workflows including data preprocessing, augmentation, hyperparameter tuning, and performance profiling.
- Collaborate closely with engineers in computer vision, embedded systems, software, and UI/UX to ensure seamless integration of AI pipelines into real\-time systems.
- Produce clean, well\-documented code and maintain version\-controlled model artifacts and experiment logs.
- Write technical documentation for models, training procedures, evaluation criteria, and system integration.
Qualifications
- Bachelor's or Master's degree in Artificial Intelligence, Data Science, Computer Science, Machine Learning, or a closely related discipline.
- 2–3 years of experience in machine learning roles through internships, academic labs, or early career positions.
- Strong understanding of Convolutional Neural Networks (CNNs) for image and video\-based tasks.
- Strong understanding of transformer architectures and their applications in vision or multimodal learning.
- Strong understanding of embedding systems and vector space modeling for semantic and similarity\-based tasks.
- Strong understanding of encoding mechanisms and dimensionality reduction techniques for latent representation.
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Familiarity with pose estimation, facial recognition, or classification models (e.g., OpenPose, MediaPipe, FaceNet, ResNet variants).
- Experience training models with structured and unstructured visual datasets.
- Exposure to techniques like cosine similarity, triplet loss, contrastive learning, or temporal prediction modeling.
- Strong computer science fundamentals, including data structures, algorithms, and software design patterns.
- Comfort working in Linux\-based development environments and version control systems (Git).
- A collaborative mindset, with excellent communication skills and a willingness to learn across domains.
*Bonus (Nice\-to\-Have)*
- Experience integrating vision\-based AI models into embedded or robotics systems.
- Familiarity with ONNX or TensorRT for model optimization and deployment.
- Background in sequence modeling, recurrent architectures, or video\-based action recognition.
- Exposure to multimodal AI systems that blend image, pose, and metadata representations.
- Familiarity with techniques like CLIP, DINO, or self\-supervised representation learning.
- Experience with MLOps or training orchestration tools such as MLflow, Weights \& Biases, or DVC.
*Other Requirements*
- Must be a US Citizen or valid Green Card holder. Visa sponsorship is not available for this role at this time.
- Candidates must reside within a commutable distance of Santa Monica, California.
Additional Information
Location: Santa Monica, CA
Work arrangement: On\-site
Contract type: Full\-time
Experience level: 1–2 years
Compensation: $100,000 to $120,000 per year
Benefits: Comprehensive health coverage and flexible PTO
What Our Client Offers
- Full health coverage.
- A collaborative and intellectually driven team environment.
- Flexible PTO.
- The opportunity to work on cutting\-edge AI systems supporting mission\-critical applications.
How to Apply
This search is being conducted confidentially on behalf of our client by Escalon Recruiting. To apply or learn more, please contact us directly, the identity of the hiring company will be shared with qualified candidates as the process progresses.
Applicant Tracking Software by ExactHire: 61z92voyrzp41ledjmq3
Salary Context
This $100K-$120K range is in the lower quartile 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 Escalon, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($110K) sits 49% below the category median. Disclosed range: $100K to $120K.
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.
Escalon AI Hiring
Escalon has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Monica, CA, US. Compensation range: $120K - $120K.
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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