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About This Role
ABOUT ELEMYNT
ELEMYNT is an early\-stage startup built by Xora Innovation. We develop applied intelligence that brings AI into the real world. Our platform combines advanced machine learning, high\-performance simulation, and modern software engineering to accelerate the design, validation, and deployment of new materials. Our work sits at the intersection of AI, physics, and large\-scale computation. The problems are hard, the stakes are high, and the impact is tangible.
ABOUT THE ROLE
This role builds and operates the data and machine\-learning infrastructure the platform runs on: the pipelines that turn large\-scale scientific output into data models can train on, and the systems that move those models from research into production and keep them running there. Hands\-on work, close to both the data and the models.
You’ll own both sides. On the data side, that’s pipelines and formats that keep large\-scale output fast to query and ready for training. On the model side, it’s the packaging, serving, monitoring, and CI/CD that let models ship safely and stay healthy once they’re live. And because the platform runs inside customers’ own secure environments, on their clusters, in their cloud, or a mix of the two, whatever you build has to stay observable and reliable in places you don’t operate.
Everything downstream depends on this layer. When it’s slow or unreliable, so is everything built on top of it.
WHAT YOU WILL DO
- Build the data pipelines that ingest, transform, and curate large\-scale scientific output into efficient, training\-ready formats on object storage.
- Make that data fast to query and cheap to reuse, so analysis and downstream jobs aren’t left waiting on it.
- Build the ML data pipelines for training, fine\-tuning, and reinforcement learning: curation, deduplication, formatting, and the evaluation sets that keep training honest.
- Catch bad data early, with validation and quality gates that check schema, distribution, and completeness before it reaches a model.
- Package, version, and deploy models across development, staging, and production, with registries and reproducible builds that keep every deployment traceable.
- Run models through CI/CD and serving workflows for batch, online, and asynchronous inference, with safe rollout, rollback, and quick diagnosis when something breaks.
- Monitor deployed models for drift, degradation, latency, and anomalies, with automated regression checks that flag trouble before users do.
- Stand up dashboards, metrics, logs, and alerts that surface data and model problems while they’re still small.
- Design the APIs, services, and internal tools that make these workflows reliable and easy for engineers and scientists to use.
WHAT WE ARE LOOKING FOR
- Bachelor’s or Master’s degree in Computer Science or a related engineering field, and 6\+ years building and shipping production software, with real depth across data systems and ML infrastructure.
- Strong Python, and a track record of shipping reliable systems end to end that other people end up depending on.
- Hands\-on experience with large\-scale data systems: object storage, efficient columnar and array data formats, and distributed query and compute engines.
- Experience building data and ML data pipelines: ingestion, transformation, curation, and the validation and quality gates that catch problems before they reach training or inference.
- Production MLOps experience: packaging, versioning, serving, and monitoring models for drift, latency, and anomalies, backed by model registries and CI/CD for ML.
- Deep hands\-on experience with containers and orchestration (Docker, Kubernetes) and workflow orchestrators such as Airflow, Dagster, Flyte, or Temporal.
- Experience instrumenting production systems and using their telemetry, logs, and metrics (Prometheus, Grafana, OpenTelemetry, or similar) to debug real incidents.
- Comfort working across cloud and HPC, including distributed multi\-GPU, and owning ambiguous systems end to end in an early\-stage setting with little scaffolding.
NICE TO HAVE
- Data\-quality and governance tooling such as Great Expectations or Evidently, plus data contracts, lineage, metadata catalogs, or reproducibility tooling.
- Model\-serving patterns for high\-throughput or asynchronous inference, and runtime uncertainty or out\-of\-distribution monitoring.
- Experiment\-tracking and model\-lifecycle tooling such as MLflow or Weights \& Biases, or serving stacks such as Ray Serve, KServe, or Kubeflow.
- Experience applying ML to scientific data, such as property prediction, generative models, or graph\-based approaches.
- It’d be a plus if you’ve worked with atomistic\-ML data tooling: Atompack, ASE\-style structure databases, extended\-XYZ datasets, or the large public corpora built on them.
- Contributions to open\-source ML or data infrastructure.
LOCATION
Singapore or United States. We’re hiring in both to reach the right person. Work model is on\-site or hybrid, set per location.
CLOSING NOTE
If you don’t tick every box but this is clearly your kind of work, get in touch.
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 Xora Innovation, 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.
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.
Xora Innovation AI Hiring
Xora Innovation has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US.
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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