Machine Learning Engineer Salary in Los Angeles — 2026 BLS Data
Salary distribution
Percentile breakdown of Machine Learning Engineer base salaries in Los Angeles.
The $155,000 median base for a Machine Learning Engineer in Los Angeles is derived from BLS OEWS May 2024 data for the closest matching SOC code — 15-2051 (Data Scientists) — applied to the Los Angeles-Long Beach-Anaheim metro area, adjusted for the observed wage premium that ML Engineering titles command within that occupational bucket. The BLS national median for Data Scientists in May 2024 was $112,590; the LA metro runs approximately 15–20% above the national figure for computer and mathematical occupations, and ML Engineering titles routinely clear data science baselines by another 10–15% due to the production systems requirement. That arithmetic puts the LA ML Engineer median in the $150K–$160K range on base — consistent with Glassdoor’s $130K figure for all levels and Levels.fyi’s $308K median total comp (which includes substantial equity from FAANG and near-FAANG employers).
The median is not a target. It’s a floor for a mid-level engineer at a normal-sized employer, and a ceiling for a new grad at a startup. The actual distribution is lopsided — a handful of very well-compensated roles at entertainment tech, aerospace AI, and FAANG satellite offices drag the mean well above the median while smaller companies and research-adjacent nonprofits anchor the left tail.
What the median hides
The BLS P25-to-P90 range for ML Engineers in LA spans roughly $130K to $240K in base salary alone. That is a 1.85x spread inside a single job title in one city. Three things create most of that spread:
First, the “ML Engineer” title is used inconsistently. At a 20-person healthcare AI startup it often means someone who builds scikit-learn pipelines and writes Airflow DAGs — work that would be called a data scientist or analytics engineer elsewhere and is paid accordingly. At Netflix, Google DeepMind, or Snap, the same title requires production-scale model serving, distributed training across GPU clusters, and ownership of latency-sensitive inference infrastructure. These are functionally different jobs with a $60K-$100K base gap between them.
Second, the BLS SOC bucket 15-2051 lumps entry-level analysts who do light modeling with staff-level ML platform engineers who design the infra those analysts sit on. The BLS does not have a dedicated “Machine Learning Engineer” SOC code — these workers are spread across 15-2051 (Data Scientists), 15-1252 (Software Developers), and sometimes 15-2041 (Statisticians). Any percentile drawn from a single code undersells the top of the market.
Third, the entertainment and defense verticals that define LA’s employer mix pay differently than the pure-tech market. Disney, Warner Bros. Discovery, and Paramount use ML engineers for recommendation systems and content moderation — they pay well but typically cap out 15-25% below equivalent FAANG roles. Aerospace and defense employers (Northrop Grumman, Raytheon, SpaceX, L3Harris) often require clearances, run on government contract structures, and pay noticeably below commercial tech at senior levels, though the clearance itself eventually becomes a salary lever if you go back to dual-use AI companies.
How LA compares to other major ML hubs
Los Angeles is the fourth-largest ML job market in the US by headcount behind San Francisco Bay Area, Seattle, and New York. The pay hierarchy roughly mirrors that ranking:
San Francisco Bay Area sits at the top: $190K-$220K base median, with total comp medians above $300K for senior ICs at FAANG and frontier AI labs. The concentration of foundation model companies (Anthropic, OpenAI, Scale AI, Cohere) has compressed the gap between “working in AI” and “working at the frontier” — but you need to be at the right employers for those numbers.
Seattle runs $175K-$195K median base, anchored by Amazon’s Applied Science group, Microsoft Azure AI, and a growing cluster of AI startups. Total comp tracks SF closely when you’re at Amazon or Microsoft; the startup ecosystem pays noticeably less.
New York lands around $175K-$200K for ML-specific roles, lifted by Two Sigma, Citadel, and Bloomberg’s quant-adjacent engineering shops, which blend ML with quantitative finance. These roles pay SF-comparable or better on cash but with thinner equity profiles since the firms are private and the RSUs don’t trade on public markets.
Los Angeles at $155K median base is approximately 25-30% behind the SF Bay Area on cash and about 15% behind Seattle and NYC. On total comp — once you strip out the FAANG equity that props up the SF and Seattle numbers — the gap narrows. Mid-tier LA companies (Hulu, TikTok US, Snap, Dollar Shave Club, Bird) pay total comp $210K-$280K for senior ICs. That’s real money even if it’s not the SF headline.
The legitimate case for LA: cost-adjusted purchasing power is competitive, the entertainment, gaming, and aerospace verticals are genuinely deep here (not satellites of a coastal tech hub), and the market is less saturated with candidates holding specific entertainment AI experience.
What drives the spread: company tier, level, and specialty
Company tier is the biggest single lever.
- Tier 1 (FAANG offices, Snap, TikTok US, SpaceX): $175K-$210K base, $200K-$400K+ total comp at L4-L5 equivalent.
- Tier 2 (profitable mid-stage tech, Hulu, Riot Games, Activision Blizzard, NBCUniversal digital): $145K-$180K base, $180K-$270K total comp.
- Tier 3 (series-B/C startups, healthcare AI, fintech): $130K-$160K base, equity upside varies wildly.
- Tier 4 (pre-series-A, nonprofits, research labs without commercial funding): $100K-$135K, often with PhD stipend-equivalent framing.
Level matters as much as tier at larger companies. A rough ladder at a Tier 1 employer:
- L3 / entry (0-2 years): $160K-$185K total comp
- L4 / mid (2-5 years): $220K-$310K total comp
- L5 / senior (5-9 years): $300K-$420K total comp
- L6 / staff (9+ years): $420K-$600K total comp
- L7 / principal: $600K-$900K total comp
The jump from L4 to L5 is where LA ML compensation starts pulling away from national comparisons — senior engineers at LA’s Tier 1 employers earn comfortably above national data scientist medians because equity vesting and refresh grants compound.
Specialty commands a meaningful premium over generalist ML. In 2025-2026, the skill premiums visible in LA’s job postings cluster around three areas:
Generative AI and LLM fine-tuning: Roles requiring production experience with RLHF, DPO, or LoRA fine-tuning carry a 30-40% base premium over vanilla ML engineering. The concentration of entertainment content companies exploring gen-AI tooling for script analysis, dubbing, and visual effects has created genuine local demand — not just titles rebranded from a resume trend.
MLOps and inference infrastructure: Building the systems that serve models at scale (Triton Inference Server, vLLM, Ray Serve, Kubernetes GPU scheduling) has quietly become a distinct specialty with its own pay band. Engineers who can reduce p99 inference latency or cut serving costs meaningfully are paid like senior SREs with an ML premium on top — $185K-$220K base is realistic at Tier 1-2 employers.
Computer vision for robotics and physical AI: SpaceX, Joby Aviation, and several autonomous systems startups in the Hawthorne-to-El Segundo corridor hire heavily for this. Salaries are competitive with commercial tech ($160K-$195K base) and clearance-track roles add 10-15% once the clearance is granted.
Total compensation breakdown
The $155K median base is only part of the story. A mid-level ML Engineer (L4 equivalent) at a Tier 1-2 LA employer typically sees:
- Base salary: $155,000. BLS-tracked, W-2 line 1. This is what’s reported in survey data and what most COL calculators use.
- Annual performance bonus: ~$20,000. Typically 12-15% of base at public companies, tied to individual and company performance. Startup bonuses are smaller or discretionary — some substitute additional equity.
- Annualized equity (RSUs): ~$35,000. At Tier 1 employers, a mid-level initial grant of $120K-$160K over four years works out to $30K-$40K annualized at grant-date price. Year 2-3 refresh grants often increase the effective annualized figure to $50K-$70K for strong performers.
Total: approximately $210,000 all-in at the median for a Tier 1-2 employer. At Snap or TikTok US — two of the heavier ML employers in LA proper — mid-level total comp runs $250K-$330K when equity is included.
Signing bonuses are common at Tier 1 employers, typically $15K-$40K at L4, $40K-$80K at L5+. They’re paid in year 1 only and almost always carry a 12-month clawback provision. At Tier 3 startups, signing bonuses are rare; instead you see larger equity grants with 4-year vesting and a 1-year cliff.
One component unique to LA’s entertainment sector: some employers (notably streaming companies and major studios) offer deferred compensation plans or profit participation structures that don’t appear in standard salary benchmarks. These can add $10K-$30K annually for engineers working on platform or monetization-adjacent ML but are illiquid and company-specific — factor them in carefully.
Cost-of-living adjusted reality
Los Angeles carries a cost-of-living index of approximately 149 (US average = 100), meaning average expenses run about 49% above the national baseline. Housing dominates: the median asking rent for a one-bedroom apartment in LA is roughly $2,400-$2,800/month in 2025, compared to $1,200-$1,400 nationally. That gap alone accounts for most of the COL premium.
Adjusted purchasing power math: a $155K LA base has the same purchasing power as roughly $104K at the national average. To match $155K of LA purchasing power, a city at the national average only needs to pay $104K. The practical implication — if you’re evaluating a remote offer benchmarked to national rates, $125K-$135K remote buys similar lifestyle purchasing power to $155K in LA.
The SF comparison is more instructive. San Francisco’s COL index is approximately 179. A $220K SF base adjusts to ~$123K at national purchasing power — slightly better than a $155K LA base ($104K) but not dramatically so. The engineer who moves from LA to SF for a $65K base raise is capturing roughly $19K of real purchasing power after costs, not $65K. That’s still a meaningful improvement but it recalibrates the decision.
Where the COL model breaks down: housing costs are somewhat fixed regardless of income level above a threshold. A $155K LA engineer paying $2,600/month for a decent one-bedroom is spending 20% of gross on rent. A $240K LA engineer (P90) in the same apartment is spending 13%. At the high end of the LA pay range, the COL penalty shrinks because housing doesn’t scale with earnings. That’s the arithmetic argument for staying in LA, pushing for senior titles, and not moving to SF for a mid-level role.
Three-lever negotiation playbook for LA ML roles
Lever 1: Anchor to total comp, not base. Most LA ML recruiters present an offer as “base salary + bonus target + initial equity grant.” The equity number is usually presented as a four-year grant total ($120K-$160K), which looks larger than the annualized equivalent ($30K-$40K). When evaluating competing offers, always convert everything to annualized total comp — this is the only apples-to-apples comparison and it’s what Levels.fyi data reflects. If the recruiter presents equity as a four-year total, acknowledge it, then say: “To make sure I’m comparing correctly, can you confirm what the annualized value comes to at current share price?” It signals sophistication and resets the conversation to numbers you can actually reason about.
Lever 2: Use California pay transparency law. California SB 1162, in effect since January 2023, requires employers with 15+ employees to include salary ranges on job postings. This is not theoretical leverage — it’s actionable data. If you’re applying to a role at a company covered by the law, the posted range tells you the approved budget for the role. If the offer comes in below the posted midpoint, you have a factual basis for pushing back without referencing competing offers. The law also means LA employers have internalized that candidates will see the band — recruiters are less likely to anchor low on the first offer the way they could pre-2023. Still, initial offers typically come in at the lower half of the posted range; countering to the midpoint is almost always reasonable.
Lever 3: Compete the offer on specialization, not seniority. The fastest way to move a mid-level ML offer in LA is to surface a specific technical differentiator that maps to the team’s immediate problems — not to claim you should be leveled higher. “I have three years of production experience with vLLM and Triton for low-latency serving, and I can see from the job description that’s a core focus for this team” is a much stronger position than “I think I should be an L5 rather than L4.” The former is about value delivered; the latter is about status. In a market where gen-AI and MLOps skills are genuinely scarce, specialization claims backed by specific production examples are believable negotiation grounds. Prepare two or three measurable examples — inference latency reductions, compute cost savings, model accuracy improvements on production distributions — that quantify your specialty’s ROI.
Data caveats worth knowing
BLS OEWS is the most rigorously collected public compensation dataset available — mandatory employer reporting across tens of millions of workers — but four caveats are material when applying it to ML Engineering in LA:
No dedicated ML Engineer SOC code. BLS folds ML Engineers into Data Scientists (15-2051), Software Developers (15-1252), and occasionally Computer and Information Research Scientists (15-1221). The percentiles on this page weight toward 15-2051 (Data Scientists) because it’s the best single-code proxy, but some ML Engineer populations are counted in 15-1252. The cross-code blending tends to understate the top of the LA ML pay range by $20K-$40K at the P75-P90 level.
Equity is excluded by definition. BLS captures wages paid — W-2 box 1 income. RSUs are captured only when they vest and are reported as wages; initial grant totals don’t appear in OEWS data. For Tier 1 LA employers where equity is 20-35% of total comp, BLS systematically understates the economic package.
The May 2024 survey has a data lag. By the time you read this in 2026, the AI engineering market has moved. The BLS data captures salaries as paid in May 2024 — at least 24 months old. The generative AI hiring surge of 2023-2024 has likely pushed P75 and P90 figures higher, particularly for LLM and inference infrastructure specialists.
LA startup and entertainment industry roles are underrepresented. OEWS sampling weights toward larger, more established employers who file QCEW reports reliably. The growing LA AI startup ecosystem — production AI companies in Culver City, Santa Monica, and Playa Vista that have raised $50M-$500M — tends to underappear in the data. Their pay practices can diverge meaningfully from the incumbents captured in the survey.
For the most current ML Engineer compensation benchmarks in LA, cross-reference this BLS data with Levels.fyi (which shows LA median total comp around $308K for self-reported ML Engineer roles at tech companies), California job posting salary ranges under SB 1162, and recruiter screen conversations — the combination of all three gets you within 10-15% of any specific offer you’ll actually see.