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About This Role
Location: New York, United States (Office) \- Must be able to work office based in NYC
On\-Site \| Full\-time
Compensation: $300K \- $375K
Our client, a premier venture accelerator backing high\-growth technology and emerging\-tech startups, is seeking an experienced and self\-directed Senior Machine Learning Engineer to join their in\-house engineering team in New York City.
Reporting directly to the Chief Technology Officer (CTO), the Senior Machine Learning Engineer will take full ownership of features throughout the product lifecycle—from requirements definition to production deployment. This onsite role requires an entrepreneurial mindset and deep technical execution to turn loosely defined problems into robust, production\-ready machine learning and LLM\-based systems without reliance on large engineering teams or dedicated project managers.
Key Responsibilities
- Own Applied ML End\-to\-End: Translate ambiguous, high\-level business problems into datasets, experiments, models, and production systems independently.
- Build Production Pipelines: Develop and maintain production Python systems for data collection, enrichment, feature extraction, scoring, model evaluation, and AI\-assisted research workflows.
- Model Design \& Evaluation: Define labels and features, construct evaluation sets and backtests, detect data leakage, evaluate source quality, and select optimal modeling approaches (including traditional ML and LLMs).
- Production Deployment \& Operations: Transition models from research to production environments, managing artifacts, feature/prompt compatibility, APIs, background jobs, observability, failure handling, and release cycles.
- Enhance LLM Infrastructure: Improve large language model systems, including structured data extraction, research agents, prompt and model evaluation, and safety guardrails for untrusted external inputs.
- Stakeholder Collaboration: Work directly with key stakeholders to determine roadmap priorities, clearly articulate model behavior and tradeoffs, and iterate iteratively based on real\-world usage.
Requirements Basic Qualifications
- Senior\-Level ML Expertise: Proven ability to drive complex, ambiguous ML problems from initial experimentation through to reliable production releases.
- Production Python Proficiency: Strong mastery of Python across data exploration, training pipelines, application logic, APIs, and production debugging.
- Robust Modeling Judgment: Deep experience in problem formulation, label definition, feature engineering, evaluation metrics, backtesting, data leakage prevention, calibration, interpretability, and model selection.
- Full\-Stack ML Engineering Capability: Strong software engineering and data pipeline fundamentals to deploy, integrate, schema\-manage, and monitor services autonomously.
- Applied LLM Systems Experience: Hands\-on experience with structured outputs, model/prompt evaluation frameworks, observability, retry strategies, cost/latency optimization, and input validation.
- Product Sense \& Communication: Ability to communicate technical tradeoffs clearly with non\-technical stakeholders and translate model outputs into actionable business tools.
- Location: Based in or willing to relocate to New York City (onsite presence is strictly required).
Preferred Qualifications
- Track record of shipping customer\-facing ML products with end\-to\-end ownership.
- Strong portfolio of technical work (e.g., active GitHub, open\-source contributions, published research, or technical writing).
- Prior experience developing prediction, ranking, classification, recommendation, or anomaly\-detection systems on messy, real\-world data.
- Background building LLM evaluation frameworks, structured extraction pipelines, or automated research agents.
- Early\-stage startup experience as a founder, early ML hire, or senior IC working without dedicated platform teams.
- Exceptional technical or quantitative pedigree (e.g., strong research background, competition achievements, or top\-tier academic background in quantitative disciplines).
Benefits
- Direct partnership with high\-impact founders across emerging technology sectors.
- High\-trust, high\-autonomy environment within a lean, elite engineering team composed of industry veterans.
- Direct ownership over core systems shaping founder discovery and operational workflows.
- Accelerated career growth and networking opportunities within a leading startup ecosystem.
- Competitive compensation and benefits package.
Interview Process
- Hiring Manager Interview
- Technical Interview
- Behavioral \& Culture Interview (with the CPO \& Co\-Founder)
- Final Interview (Technical Coding Assessment)
Due to the high volume of applications we anticipate, we regret that we are unable to provide individual feedback to all candidates. If you do not hear back from us within 4 weeks of your application, please assume that you have not been successful on this occasion. We genuinely appreciate your interest and wish you the best in your job search.
Commitment to Equality and Accessibility:
At MLabs, we are committed to offer equal opportunities to all candidates. We ensure no discrimination, accessible job adverts, and providing information in accessible formats. Our goal is to foster a diverse, inclusive workplace with equal opportunities for all. If you need any reasonable adjustments during any part of the hiring process or you would like to see the job\-advert in an accessible format please let us know at the earliest opportunity by emailing human\[email protected].
MLabs Ltd collects and processes the personal information you provide such as your contact details, work history, resume, and other relevant data for recruitment purposes only. This information is managed securely in accordance with MLabs Ltd’s Privacy Policy and Information Security Policy, and in compliance with applicable data protection laws. Your data may be shared only with clients and trusted partners where necessary for recruitment purposes. You may request the deletion of your data or withdraw your consent at any time by contacting [email protected].
Salary Context
This $300K-$375K range is above the 75th percentile 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 mLabs, 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. This role's midpoint ($337K) sits 57% above the category median. Disclosed range: $300K to $375K.
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.
mLabs AI Hiring
mLabs has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $350K - $375K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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