ML Engineer Jobs in New York: The 2026 Market, Real Comp

NYC currently posts 1,200-3,000+ Machine Learning Engineer roles depending on the aggregator, with base salaries running from $105K (entry-level) to $245K+ for staff engineers and total comp pushing past $400K at top labs. The real market splits into four distinct sub-economies, and which one you target changes the playbook entirely.

The NYC ML market in numbers (May 2026)

Built In NYC's anonymized salary data puts the average base for a Machine Learning Engineer in New York City at $151,101, with the most common range $130K-$140K and an overall range of $118K-$201K [^f1]. By experience: under 1 year reports about $105,000; 7+ years reports $245,333 [^f2]. Built In NYC's listings page shows a wider role range — $77K to $368K — across 26 pages of postings spanning intern to executive [^f5].

The aggregator volume looks loud. Glassdoor lists 1,273 NYC ML engineer roles. Indeed lists 1,941. LinkedIn shows 3,000+ for ML engineer specifically and 5,000+ for broader machine learning roles [^f4]. Single roles overlap heavily across boards — a Spotify posting appears on five aggregators at once — so the unique role count is closer to 600-900 in any given week.

The gap between the average and the top of band tells you the actual story. Indeed lists Spotify's NYC Senior Staff ML role at $281K-$401K base. Google's early-career PhD ML role posts at $147K-$211K. Genentech's NYC entry-level ML role posts at $160K-$310K [^f3]. The "average $151K" number averages over scale-ups and labs together. The labs and the elite fintech seats sit far above that line.

The four NYC ML sub-economies — different jobs, same title

Most "ML engineer NYC" articles treat the market as one thing. It isn't. There are four distinct sub-economies in NYC that all use the same title, and they're not really competing for the same people.

Frontier AI labs. Anthropic, OpenAI, Google DeepMind, and Meta FAIR all run NYC presences. The work is research-adjacent, the hiring bar is calibrated to research-engineer talent, and the comp clears $300K base for senior IC roles plus material equity. This tier hires through referrals, internal moves, and a small slice of inbound from public-facing model releases. The careers page is the cover, not the door.

Fintech quant. HRT, Two Sigma, Citadel, Jane Street, DE Shaw, and a handful of newer pods. The "ML engineer" title here is mostly a quant-research-with-ML role. Base + bonus + sign-on totals can clear $500K for mid-level engineers with a few years in. The funnel is technical: in-house recruiter outreach, then a multi-round technical screen anchored on probability, linear algebra, and applied ML. Different game, different prep.

Scale-up AI-product. The rocket-ship Series B-D layer — companies shipping AI features into product, not building foundation models. ML engineers here own retrieval, ranking, evals, fine-tuning, and the surrounding data infra. Base $180K-$280K with equity that matters if the company executes. Hiring is founder-led at the senior end, recruiter-led at the mid level. The 250-applicant stack [^f7] is real here.

Seed-stage AI startups. Founders hiring their first or second ML engineer. Comp clusters at $150K-$220K base with the equity grant doing the lifting. Hiring is almost entirely Twitter, founder mutuals, ex-coworker network, and matched intros. If you're applying through Greenhouse to a six-person AI startup, you're in the wrong channel.

Each tier has its own playbook. Pick the tier first, then work backward.

What the comp data actually shows

The headline number — $151K base average for NYC ML engineers — is technically accurate and structurally misleading.

Here's what the data underneath shows. Built In NYC reports $105,000 base for ML engineers with under 1 year of experience and $245,333 base for those with 7+ years [^f2]. The role range across all NYC ML postings on Built In spans $77K to $368K [^f5]. Indeed posts Spotify Senior Staff at $281K-$401K, Google early-career PhD at $147K-$211K, and Genentech entry-level at $160K-$310K [^f3].

Hot take: the "average" hides a strongly bimodal distribution. There's a $130K-$180K cluster at scale-ups and recently-funded AI-product companies, and a separate $250K-$400K+ cluster at frontier labs and fintech ML seats. The middle is thinner than the average suggests. Knowing which cluster you're targeting changes both the negotiation strategy and the channel mix.

Total comp matters more than base for the top tier. The Spotify Senior Staff posting at $281K-$401K base likely lands at $500K+ total once equity and bonus are factored in. The fintech quant ML role at HRT or Two Sigma can clear $500K for mid-level talent with the right model-portfolio fit. Comparing only base across these tiers is the wrong frame.

The hiring channel breakdown

The corporate job posting receives roughly 250 applications on average; entry-level roles often pull 400+ [^f7]. The applicant-to-interview ratio collapsed from 15.25% in 2016 to 8.4% in 2023 to 3% in 2024 [^f6]. Those are market-wide numbers. For NYC ML specifically the apply-through-the-board math is even rougher because the supply of candidates with credible ML credentials has compounded faster than the role count.

Translation: applying cold through Indeed or LinkedIn for a senior ML role in NYC has roughly a 1-2% chance per application of getting you to a first call. That's the ceiling, not the floor.

By tier:

• Frontier labs. Cold applications convert at 1-2%. Referrals from internal engineers convert at 30-50%+ at the screen stage. If you have a former coworker at Anthropic, OpenAI, DeepMind, or Meta FAIR's NYC presence, your single highest-ROI move is asking for a referral.
• Fintech quant. The in-house recruiter outreach channel works because the pipeline is technical-screen-driven. The recruiters at HRT, Two Sigma, Citadel, and Jane Street source aggressively. Reply when they reach out, and prep the technical screen before you do — the bar is high and you don't get a second chance for 12 months.
• Scale-ups. The 250-applicant stack is the dominant failure mode here. Founder or CTO direct intro skips the queue. The matched-intro channel exists for exactly this — a clean profile, an engine that matches you to the right Series B-D companies, and a founder intro instead of an application.
• Seed-stage. Twitter is the dominant signal. Founder mutuals are the dominant intro path. A two-week pattern of replying thoughtfully to AI-startup CEOs on X with technical takes will generate more inbound than 200 applications.

Hot take: every channel that actually converts in NYC ML hiring is non-application-driven. The job board is a directory, not a funnel. Treat it that way.

Why the keyword "ml engineer jobs new york" is misleading

Three thousand LinkedIn results sounds like a market. It isn't. A single Spotify Senior Staff role posts on Indeed, LinkedIn, Glassdoor, Built In, and ZipRecruiter. Five entries, one job. The aggregator stack double-counts at every layer.

The real unique-role count for ML engineering in the NYC metro is closer to 600-900 on any given week. Of those, the slice actually hiring fast is a fraction — maybe 200-400 with active interviewers in the loop. The rest are open postings the company hasn't pulled but isn't actively prioritizing.

Hot take: spending two hours apply-spraying across job boards is a worse use of time than 30 minutes building a clean profile on a matched-intro service plus 30 minutes of targeted recruiter outreach. The math is straightforward. Apply-spray converts at 1-2%. Targeted outreach to a specific recruiter at a specific company you know is hiring converts at 10-15%. Matched intros — when the matching engine has placed candidates at that company before — convert higher than that.

The board listing has one good use: signal on which companies in NYC are actively hiring ML talent right now. Read the listings to build the list. Don't apply through them.

What strong NYC ML candidates do (and what they don't)

The strongest ML engineers we represent at Standout don't run mass-application strategies. They've already learned the math doesn't favor them in a 250-applicant stack [^f7]. They run a different motion: clean profile once, targeted recruiter outreach for the labs they want, and direct founder intros for the scale-up tier.

Standout was built for that scale-up and seed-stage motion. Tech professionals build a profile once. Our matching engine surfaces companies that fit, the candidate accepts or declines, and we introduce accepted matches directly to the founder [^f8]. First matches arrive within hours of profile completion, not days [^f9]. Free for candidates. We cover all tech roles — engineering, product, design, data, ML/AI, DevOps, marketing, sales, ops, customer success, BD — at US tech companies from seed through Series D [^f10].

NYC is one of the four metros we actively cover (alongside San Francisco, Austin, LA, and remote-US). The pattern we see across NYC ML candidates: the senior engineers who land roles fast are the ones running 2-3 channels in parallel — recruiter outreach for the lab/fintech tier they care about, matched intros for the scale-up tier, and a small number of high-conviction founder DMs for the seed tier.

We're not the right tool for everyone. If your target is exclusively frontier labs, the matched-intro channel is supplementary; the dominant channel there is internal referral. If your target is exclusively fintech quant, the in-house recruiter pipeline at HRT or Two Sigma is the right primary channel and Standout is the parallel option for non-fintech inbound. For the scale-up and seed tiers — where 60-70% of NYC ML demand actually sits — matched intros are usually the best ROI.

For more on adjacent markets, see the SF AI engineer market and engineering jobs at Anthropic.

Action plan if you're searching this month

1. 1Pick the tier. Frontier lab, fintech quant, scale-up, or seed. Different playbooks. Don't run the seed-stage Twitter motion on a frontier lab and don't run the lab-referral motion on a six-person seed startup.
2. 2For the labs: find one engineer at the lab from your network and ask for a referral. Cold applications at this tier convert at 1-2%; referrals convert at 30%+. The one ask compounds.
3. 3For fintech quant: build the technical interview reps before you talk to the recruiter. Probability, linear algebra, applied ML. The bar is the screen.
4. 4For scale-ups: set up matched intros so you skip the 250-applicant stack. Building a clean profile on Standout's matching engine takes 20 minutes and gives you a channel where you're not in a stack.
5. 5For seed-stage: Twitter and founder mutuals dominate. Reply thoughtfully to AI-startup CEOs on X for two weeks. The inbound shifts.

Use the open-board churn (Built In NYC, startup-focused listing sites, LinkedIn) for signal on which companies are hiring. Don't use it as your application channel. The 1-2% per-application math is the structural state of the funnel, not a personal failing.

FAQ

How much do ML engineers make in NYC?

Built In NYC's average base is $151,101, with under-1-year engineers at about $105,000 and 7+ year engineers at $245,333 [^f1][^f2]. The role range across NYC postings spans $77K to $368K [^f5]. Top-of-band roles like Spotify Senior Staff post at $281K-$401K base [^f3], with total comp typically clearing $500K once equity is included.

Which NYC companies hire ML engineers most?

Across the listings, the top hirers cluster into four buckets: frontier labs (Anthropic, OpenAI, Google, Meta FAIR), fintech quant (HRT, Two Sigma, Citadel, Jane Street, DE Shaw), scale-up AI-product (Spotify, Genentech, Ramp, and the broader Series B-D layer), and seed-stage AI startups [^f3][^f5]. Built In NYC alone runs 26 pages of ML postings spanning intern to executive [^f5].

Are ML engineer jobs in NYC remote-friendly?

Mixed. Frontier labs and fintech quant are largely in-office, four to five days. Scale-ups split — some are full remote, many are hybrid 3 days in NYC. Seed-stage AI is overwhelmingly in-office in NYC right now because founders prioritize co-location for ML iteration speed.

Is it better to apply through LinkedIn or directly?

Neither, for senior roles. Cold applications convert at 1-2% across channels because every posting receives roughly 250 applications [^f7] and the applicant-to-interview ratio collapsed to 3% in 2024 [^f6]. Direct founder intros, internal referrals, and matched-intro services convert at multiples of that.

How fast can a senior ML engineer land a role in NYC?

Variable, but the channel choice dominates. The senior ML engineers we work with at Standout often land first matches within hours of profile completion [^f9]. End-to-end (intro to offer) for the strongest senior candidates running 2-3 channels in parallel typically closes in 3-6 weeks. Pure cold-application strategies often take 90+ days for the same outcome.

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[Skip the 250-deep ML stack. Get matched directly.](https://standout.work) Standout matches NYC ML engineers with hiring companies based on your profile and intros you to the founder when there's a fit. Free for candidates. First matches in hours.

[^f1]: Built In — NYC ML Engineer Salaries 2026. [^f2]: Built In — NYC ML Engineer Salaries 2026 (years-of-experience breakdown). [^f3]: Indeed — NYC ML Engineer Listings (May 2026). [^f4]: LinkedIn / Glassdoor / Indeed — NYC ML role counts (May 2026). [^f5]: Built In NYC — ML Job Listings. [^f6]: HiringThing — 2025 Job Application Statistics (interview-rate trend). [^f7]: HiringThing — 2025 Job Application Statistics (250 applications per posting). [^f8]: standout.work positioning page. [^f9]: standout.work matching speed. [^f10]: standout.work coverage breadth.